Ecommerce Analytics in India – An Overview

Ecommerce is an active and budding industry in the Indian market. Thousands of Indians buy and sell products online every day. Here, we’ll discuss what ecommerce analytics is and how it can promote better results for online store owners in the country.  The ecommerce industry in India has been growing exponentially for the last few years. Many Indians prefer online shopping and home deliveries, be it groceries, electronics, furniture, or high-end items.  According to IBEF (India Brand Equity Foundation), the Indian ecommerce market is expected to reach 350 billion USD by 2030. We currently have 125 million online shoppers and another 80 million are estimated to add to the count by 2025. The Digital India program has nudged many businesses to expand their stores to electronic marketplaces.  With such growth in the ecommerce industry, it’s no surprise that business owners are investing in online analytical tools to derive useful insights from data and make better decisions to boost sales. SMBs and large online retailers are partnering with ecommerce analytics service providers to digitally transform their businesses and increase ROI.  In this blog, we’ll read more about e-commerce analytics and its role in the Indian markets.  What is Ecommerce Analytics? Ecommerce analytics is the process of collecting, analyzing, and interpreting data from online marketplaces, digital stores, electronic transactions, etc., to make data-driven decisions. The data is collected from various internal and external sources like website traffic, social media reactions, ad clicks, online sales, product/ brand searches, and much more.  There are many tools for Ecommerce analytics providing in-depth insights and graphical reports for retailers and store owners to easily understand customer behavior and market trends. From creating a robust marketing strategy to forecasting market demand, analytical insights can help an ecommerce business in many ways. While tools like Google Analytics are helpful, an ecommerce business needs tailor-made solutions to derive actionable insights in real-time. Partnering with reputed Ecommerce development companies in India helps business owners streamline their data (and data sources), create seamless workflows, automate recurring tasks, monitor daily activities, and enhance customer experience Types of Data Used in Ecommerce Analytics Data is crucial for analytics. In the ecommerce industry, four types of data are used by businesses to gain a deeper understanding of the target audience, markets, customers, and competitors.  Zero Party Data  Zero-party data refers to the data a business directly collects from the target audience. It could be in the form of surveys, quizzes, feedback, etc. The audience voluntarily shares this data with the business, which is then analyzed using an ecommerce tool.  First Party Data  First-party data belongs to the ecommerce business and is extracted from website traffic, in-store visits, interactions with customers (online and offline), etc. The customers are aware that their data (preferences, behavior patterns, interests, etc.) are collected by the business.  Second Party Data Second-party ecommerce data is collected and owned by other organizations which share the data with the businesses. This includes data from affiliates, business partners, etc., and provides a sneak peek into customer behavior, purchase habits, and market demand for different products.  Third-Party Data  Third-party data is collected by organizations that are not connected or affiliated with the ecommerce business. It includes market research firms, analytical service providers, data brokers, etc., who sell customer data to businesses.  KPIs for Ecommerce Analytics When implementing data analytics in ecommerce and retail, KPIs (key performance indicators) have to be selected carefully. These metrics help store owners know how their business is performing and find ways to improve the results. While an ecommerce business has many KPIs to track, the following is a must.   Discovery Rate The discovery rate is the percentage of users who discover or visit the ecommerce website for the first time. It shows whether the efforts to attract new customers are being successful. It also helps determine which channels are delivering the expected results and where the business can find more opportunities.  Acquisition Rate Acquisition rate is another important KPI for digital commerce analytics. It shows the number of visitors who completed the necessary actions on the website. It is derived by dividing the total number of people who became customers from the total audience. This puts the customers in the sales funnel.  Conversion Rate In some instances, the conversion rate is the same as the acquisition rate. However, conversions are considered subjective, while acquisitions have specific criteria. This changes from one marketing campaign to another and what the business wants.  Customer Retention Rate As the term suggests, the customer retention rate is the percentage of customers who remain with the business and initiate multiple purchases over time. ecommerce analytics helps find ways to improve the retention rate by creating customer loyalty programs, referral schemes, etc. A higher retention rate implies greater success.  Use Cases of Ecommerce Analytics Customer LTV  By using the analytical tools of ecommerce, businesses can process customer data to determine their value over their lifetime (interaction with the store). A high-value customer is someone who generates more sales and repeat purchases and adds value to the business. Retailers can create highly targeted campaigns, deals, and offers for such customers.  Customer Behavior  Customer behavior analytics deals with understanding how a customer interacts with the business – website visits, products they check often, factors that influence their decisions, CTAs (call to action) they respond to, and so on. It helps in product development, better marketing strategies, and showcasing the right product to the right customer.  Customer Segmentation Segmenting customers based on demographics, interests, age, gender, purchasing habits, etc., can be done through ecommerce analytics. It helps in developing personalized marketing campaigns for each segment and reaching out to them through their preferred choice of communication channels.  Sales Performance  By investing in the best sales analytics software, businesses can get comprehensive insights about the sales generated for the given period, estimate sales for the coming times, the difference between targets and achievements, and how to improve sales.  Fraud Detection  ecommerce analytics is also helpful to minimize the risk of fraudulent transactions by analyzing customer and sales data. The software will send alerts for potential risks, which help in devising strategies to safeguard the business and the interests of genuine customers.  Revenue Management  ecommerce businesses can rely on the best revenue analytics software to track their earnings and expenses. Store owners can get insights about where most money comes from and where it is being spent, the soft areas for improving MRR,

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5 Hidden Costs in Manufacturing Exposed by Managed Analytics

Manufacturing is the most challenging aspect of any business. Several factors affect manufacturing such as accuracy, focus on details, and economical use of resources. But, there is more hidden and challenging information to identify significant components. Metrics like quality, dependability, and flexibility are included in this. To find the exact metrics and make informed decisions every business requires managed analytics services. Finding this true cost of the product involves identifying hidden costs and analyzing the total cost of Ownership (TCO). TCO includes all direct and indirect expenses such as manufacturing, buying, shipping, design, inventory control, labor inefficiencies, and even refunds. However, it also provides the most exact statistics to compare different production technologies, such as 3D printing and injection molding. What is Manufacturing Analytics? Manufacturing analytics solutions are used by manufacturers to track KPIs, decrease unplanned downtime, increase customer satisfaction, and optimize production efficiency. Industry 4.0, or smart manufacturing, is the term used to describe the larger trend. To do this, data gathered from industrial machinery and traditional IT systems must be combined, and analytics software must be used to make better decisions. Additionally, by predicting bottlenecks managed analytics for the supply chain in manufacturing processes that could interfere with order fulfillment, producers can determine the underlying reasons for production mistakes. 5 Hidden Costs in Manufacturing exposed by Managed Analytics With the help of managed analytics as a service, and using managed BI for smart factories, businesses can now automate the process of converting data into insights, which will help them further accomplish objectives like successful customer retention and the ability to forecast customer behavior through predictive modeling and 360 Degree Customer View. We have covered several important subjects in our blog during the past year. This has involved deciding what has to be improved; that is, figuring out all the possible areas to enhance, picking the crucial ones, and seeing those through to the end. For your improvement activities to produce any meaningful results, this is vital. The key features that set apart businesses that truly improve are knowing what to do and having the discipline to follow through without spreading your available resources. No amount of skill, managerial ability, or procedural expertise can bring about meaningful change in the absence of these qualities. Numerous hidden expenses exist. While some are particularly detrimental in high-product-mix processes like customized fabrication, others are inherent in practically any value-producing operation. Furthermore, if these expenses are not identified and addressed, they frequently complicate other improvement initiatives. Let us now discuss the ones that are most relevant to high-product-mix operations. Information Mistakes or Errors It might be very overwhelming to have all the information you need on what to construct when to build it, what to order when to order it, dates and quantity changes, revision levels, acceptable quality, and a lot more. Many things can go wrong. The more information there is, the more varied it is, and the more likely it is that something will go wrong if it is not expressed effectively. The information problem causes numerous forms of waste. That might be a monster. The quantity and diversity of information in a high-product-mix store are unlikely to alter. You have no control over that. However, you can make improvements by converting the input data into a standard output format that is highly accurate, understandable, and actionable. The idea is to create a system that feeds accurate data to the real processing operations, particularly those that take place on the factory floor. Correctness, clarity, timeliness, understandability for all participants, and identical actionability — the ability to produce the same action independently of the person performing it — are all requirements for the information. This instruction seems quite lofty. However, upon performing a root cause analysis to determine what went wrong and what causes needless expenses, you will nearly always discover that information errors are at the top of the list. It’s also possible to overcome the majority of these problems with the help of manufacturing data warehouse services or managed analytics. Here’s a basic yet typical example: A new machine that you recently purchased can process parts 50% faster than the previous one. Isn’t it an improvement? Not if you build to the incorrect revision, overbuild, or build the incorrect thing at the incorrect moment, that is. The costs of correcting the situation will outweigh the throughput gain. And those expenses will be forgotten. These overhead costs, however, hidden, are actual, cumulative, and unavoidable if nothing is done about them. Searching Because they follow the routine as usual, search costs are hidden. We are used to watching ourselves and other people engage in the common conversation that begins with the phrase “Let’s see… where can that be?” It’s entirely avoidable, fully typical, and also quite expensive. The one benefit of search waste, which is a type of downtime and information waste, is that it is comparatively simple to improve. Eighty percent of it can be resolved with any long-term 5S/visual workplace strategy. It is one of the few enhancements that is theoretically simple to start. The execution of the sustaining plan is the sole area in which knowledge is needed. It’s difficult to discover good reasons to tolerate search waste and its associated expenses. Because search wastes time, they typically manifest as variations in efficiency and collectively cause schedule problems and delayed orders. Overexposure to Material and Labor Movement It is rather typical to move materials around in a plant. Moving people around is also common. However, moving objects and people excessively has an impact that is almost identical to searching excessively. It’s one more traditional waste that lean concepts have found. It can need changes to the structure of plants and equipment, strict adherence to quantity and timing guidelines, and an emphasis on part flow, which makes it more difficult to eliminate. However, much like search waste, it can be greatly improved. To find the best answer, experience is undoubtedly necessary. However, progress can be accomplished gradually. Two excellent examples are

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10 Ways Analytics Managed Services Will Change Your Business in the Next 5 Years

The increasing complexity of data management and the constant advancements in technology, managing and extracting insights from data may seem like a daunting task. However, analytics managed services can step in to meet the growing demands of data management. Regardless of your role as a CEO, IT head, or CXO, you’re keenly aware of the vital role data-driven decision-making plays in today’s business landscape. So here, we will delve into some transformative ways in which Analytics Managed Services are meant to reshape your business in the coming five years. Enhanced Data Governance Effective data governance is the foundation of successful data analytics. Analytics Managed Services provide robust data governance, ensuring that your data is secure, compliant, and readily available. This guarantees data integrity and mitigates risks, allowing you to focus on strategic objectives. Streamlined Data Architecture Data architecture can be complex and overwhelming, especially as your business grows. Managed Analytics Services offer expertise in data architecture, helping you design and maintain a robust infrastructure. This ensures that your data is organized, accessible, and scalable as your business expands. Establishment of a Data Warehouse As data volumes grow, traditional data warehousing may become inadequate. Managed Analytics Services can help you migrate to modern, cloud-based data warehousing solutions. This transition not only saves costs but also offers the flexibility needed for agility and growth. What’s a Data Lake? A Data Lake can be a treasure trove of insights when managed effectively. Managed Analytics Services help you set up and maintain a Data Lake, making it a valuable resource for your data analytics initiatives. This keyword-influenced strategy will be a game-changer in data management. Data Science Analytics Managed Services Data science is at the heart of making sense of data. Managed Analytics Services provide access to a pool of data scientists who can develop advanced machine learning models and algorithms, helping you extract valuable insights and predictions from your data. Big Data Implementation Big data is no longer a buzzword but a reality for many businesses. Managed Analytics Services assist in harnessing the power of big data by offering the expertise required to handle vast datasets and extract actionable intelligence from them. The Right Partner Choosing the right partner for Analytics Managed Services isn’t just about immediate benefits; it’s a strategic move that can take your business five years ahead of the competition. With the right consultant by your side, you’re not just keeping pace with current data trends; you’re anticipating and adapting to what’s coming shortly. By collaborating with companies that understand your long-term vision, you can leverage their expertise to unlock growth and innovation. This allows you to harness the power of data today while staying well-prepared for the data landscape of tomorrow. In essence, the right managed analytics service provider becomes your bridge to a data-driven future, ensuring that your business is not just competitive but leading the way in the next five years and beyond. The Next 5 Years… As we look ahead to the next five years, Managed Analytics Services will be one of the driving forces behind business transformation. For enterprises like yours, embracing these services will not only enhance your data management capabilities but also open up new opportunities for growth and innovation. In a world where data is king, Managed Analytics Services are your crown jewels, ensuring that your business stays at the forefront of the data-driven revolution. But what about the next 10 years? How will these services continue to evolve and change the way we do business? It’s a question worth brainstorming! The pace of technological advancement is relentless, and the data landscape is continually shifting. Will your business remain adaptive and competitive in this ever-changing environment? With Managed Analytics Services by your side, you’re equipping your business to thrive in the future. So, start exploring these services and consider how they will help your business. Originally Published on DataToBiz

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How Managed Analytics Services Enhance Customer Experience?

Have you ever wondered how businesses magically seem to know what you want, sometimes even before you do? Well, it’s not magic; it’s the power of data analytics. Do you want to know how Managed Analytics Services help you redefine the customer experiences? From interpreting large amounts of data to understanding customer needs, this blog focuses on how businesses seemingly anticipate your needs and deliver personalized meaningful experiences. In this blog, I’m going to take you on a journey to explore how managed analytics services can transform the way businesses serve you, and ultimately, how you experience their products or services. So, grab a coffee, get comfy, and let’s dive in! What Are Managed Analytics Services, Anyway? Let’s start with the basics. Managed analytics services are like having a team of data masters at your disposal but for businesses. They help companies collect, analyze, and make sense of the massive amounts of data they gather from various sources. Think of it as a behind-the-scenes superpower that organizations use to enhance their operations, and it indirectly affects you — the customer. The Heart of Customer-Centric Business In a customer-centric business, it’s not just about selling products or services; it’s about delivering a memorable and delightful experience that keeps customers coming back for more. Managed analytics solutions play a pivotal role in achieving this goal. Personalization Imagine strolling into your favorite online store, and boom! Right on the homepage, you’re greeted with a list of products that feel like they were handpicked just for you. This isn’t luck; it’s the result of analytics services at work. They use your past behavior, preferences, and even demographic data to create a personalized shopping experience. When you find what you want quickly, you’re a happy customer, right? Think about e-commerce giants like Amazon. They’re masters at suggesting products you’re likely to buy based on your past purchases and browsing history. But how do they do it? Managed analytics services are the engine behind these personalized recommendations. They analyze your data to understand your interests and behaviors, creating a tailor-made shopping experience just for you. This level of personalization goes beyond just e-commerce. Music services like Spotify and even news websites use similar techniques to keep you engaged. It’s like having your shopper, DJ, or news editor. Predicting Your Needs Before You Do Ever had Netflix recommend a show you end up binge-watching all weekend? Managed analytics services help businesses predict their future needs. Hear me out on this one! They analyze your past behavior to suggest products, services, or content that you’re likely to be interested in. It’s all about saving you time and effort by offering solutions before you even realize you need them. Predictive Analytics Let’s take the example of a food delivery app. It can use your previous orders, the time of day, and even the weather to predict what you might want to eat. So, when you open the app, your favorite cuisine is right there, ready to be ordered. It’s like they’ve read your mind, right? But it’s not just about food. Airlines use predictive analytics to anticipate delays and manage flights more efficiently. Banks use it to prevent fraudulent transactions. It’s everywhere, making our lives smoother and more convenient. Smooth Customer Support, Thanks to Analytics Imagine having an issue with a product or service and reaching out to customer support. With managed analytics services in play, the support team can access a wealth of information about your history with the company. They know what products you’ve bought, and issues you’ve faced before, and can provide tailored solutions. No more explaining your situation repeatedly; they already know you! Multi-channel Support Whether you contact customer support through chat, email, or a phone call, businesses aim to offer a seamless experience. With the help of analytics, they can track your interactions across different channels. If you start a conversation on chat and switch to a phone call, they pick up right where you left off. It’s like talking to a friend who remembers everything you’ve discussed. Moreover, analytics can identify trends in customer support queries. If multiple customers face the same issue, businesses can address it proactively, preventing further inconvenience. This means that issues get resolved faster, and you don’t have to deal with recurring problems. Streamlined User Experience When you visit a website or use an app, you want it to be smooth sailing, right? Managed analytics services play a crucial role in optimizing user experiences. They track how users interact with a website or app, identify pain points, and suggest improvements. The result? You enjoy a seamless, error-free experience. The User Journey Map Creating a user-friendly website or app is no longer guesswork. Managed analytics services help businesses understand the user journey. They analyze where users drop off or get stuck, and this insight guides improvements. For instance, e-commerce websites use analytics to enhance the checkout process. If they notice that customers abandon their carts at the payment stage, they can investigate why. Maybe it’s a complicated form or a lack of payment options. Analytics provides the data needed to identify these issues and make the necessary changes. But user experience doesn’t stop at websites and apps. Think about self-checkout kiosks at grocery stores or touch-screen menus at restaurants. Analytics ensures that these interfaces are user-friendly and efficient. Improved Product Development Businesses want to create products or services that you’ll love. Managed analytics services help in this process too. By analyzing customer feedback, usage patterns, and market trends, they can guide businesses in refining existing products or creating new ones. This means more of what you like and less of what you don’t. The Voice of the Customer Customer feedback is a goldmine for businesses. It tells them what’s working and what’s not. Managed analytics services can aggregate and analyze this feedback to extract valuable insights. For example, car manufacturers use customer feedback to design safer and more comfortable vehicles. Tech companies use it to refine software features. Even restaurants use it to tweak their

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Agile Business Development: Fostering Business Development Growth

At this moment in time, dynamic business environment, constant adaptation is not an option, it’s imperative for survival and growth. Managed Analytics Organizations (MAOs) face a critical choice: evolve or become obsolete. Agile Business Development (ABD) arises as the conductor, orchestrating a symphony of growth that empowers MAOs to navigate the dynamic market with resilience and achieve unprecedented growth. Leveraging a diverse portfolio of services, solutions, and contract-based resources encompassing the full spectrum of AI/ML, Data Science, Machine Learning, and Data Engineering organizations possess the unparalleled potential to harness the transformative power of Agile methodologies. Embracing ABD empowers MAOs with the agility to adapt and innovate seamlessly within the ever-shifting landscape of analytics. This dynamic approach enables continuous iteration, rapid response to market fluctuations, and a keen attentiveness to client feedback, in its entirety culminating in propelling business development to unprecedented heights. The inherent nimbleness of Agile practices equips MAOs with the capability to pivot strategies swiftly, refine service offerings with precision, and capitalize on emerging opportunities with laser focus. This holistic philosophy prioritizes continual learning, experimentation, and an unwavering commitment to customer-centricity, completely serving as the bedrock for sustainable and scalable growth. Ultimately, by integrating ABD into their core operations, MAOs navigate the complexities of the ever-evolving analytics industry and also flourish amidst its fluidity, achieving unparalleled growth and forging a path for continued innovation and excellence. From Monolith to Mosaic: Embracing the Power of Iteration ABD shatters the paradigm of rigid, linear strategies. It replaces them with a mosaic of iterative processes, allowing MAOs to adapt nimbly to market shifts, customer feedback, and unforeseen opportunities. This flexible approach eliminates the shackles of lengthy planning cycles, freeing MAOs to respond quickly and remain relevant in a rapidly evolving digital world. Flexibility Takes the Stage: A Collaborative Move of Agility ABD prioritizes flexibility, collaboration, and responsiveness. Rigid hierarchies are dissolved, paving the way for a flat, collaborative environment. Ideas flow freely, fostering innovation and allowing MAOs to pivot strategies, refine offerings, and seize fleeting opportunities. This collaborative move of agility ensures MAOs remain in exemplary rhythm with their customers, exceeding expectations and delivering exceptional value. Experimentation: The Fuel for Innovation’s Engine ABD is not a static state, but a journey of continuous learning and experimentation. MAOs are empowered to explore bold ideas, test their effectiveness, and learn from both successes and failures. This cycle of experimentation fuels innovation, propelling MAOs to the forefront of the industry and establishing them as thought leaders in the Managed Analytics space. Customer Centricity: The Unifying Melody ABD recognizes that the customer is the maestro of business growth. At the heart of ABD lies a deep commitment to customer-centric practices. This approach equips MAOs with the tools to gather meaningful customer insights, understand their evolving needs, and tailor their offerings suitably. By placing customer-centricity at the heart of their strategy, MAOs cultivate lasting relationships, build unwavering loyalty, and solidify their position as trusted partners in a competitive landscape. Adaptability: The Key to a Harmonious Representation The business landscape is a constantly evolving orchestra, where melodies shift and rhythms change. ABD equips MAOs with the agility to adapt to these changing dynamics. They can readily pivot strategies, re-evaluate value propositions, and embrace advanced technologies, ensuring they remain in an ideal tune with the market and continue to deliver exceptional value. ABD: A Transformation of Mindset and Measure Successfully implementing ABD is not merely adopting a methodology; it is a cultural transformation. Fostering a collaborative environment, encouraging receptive communication, and empowering teams to take ownership are the instruments that unleash the full potential of ABD. This shift in mindset allows MAOs to evolve into agile organizations, ready to seize opportunities and create a harmonious symphony of growth. Unleashing the Power of Agility By embracing ABD, MAOs embark on a transformative journey in the direction of sustainable and scalable growth. They become nimble, responsive, and customer-centric organizations, poised to lead the Managed Analytics landscape in the years to come. Stagnation is out, agility is in! Let’s turn up the volume and rock out to the rhythm of progress! Conclusion In conclusion, the union of Agile Business Development principles with the vast expertise of Managed Analytics Organizations paints a compelling vision of unparalleled potential and growth in the analytics landscape. By infusing agility, adaptability, and customer-centricity into their very core, MAOs become the vanguard of innovation, driving continuous evolution and transformation. From navigating the intricate terrain of AI/ML to embracing the power of Data Science, Machine Learning, and Data Engineering, the adoption of Agile methodologies transcends mere strategy to become a cultural beacon guiding their path to growth. With a commitment to perpetual learning, iterative refinement, and a steadfast dedication to client needs, MAOs chart a course for sustained and scalable growth. Through Agile Business Development, they can elevate their business acumen and redefine the standards of excellence within the industry, promising a future brimming with innovation, progress, and boundless possibilities.

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The Future of Managed Analytics Services for Small Businesses

The data analytics age for SMBs began in 1999 with the launch of browser-based CRM software. It has grown exponentially and is now an integral part of many small businesses. Here, we’ll discuss the role of Managed Analytics Services for Small Businesses and its future in the market. Businesses need analytics to process raw data and derive meaningful insights, discover hidden patterns, uncover new trends, and make better decisions. Data analytics can streamline internal processes and maximize profitability without compromising quality. Managed analytics services have become popular with the increasing talent gap in organizations and the global market. Small businesses cannot always afford to build a data analytics team from scratch and maintain it in the long term. Managed analytics services offer a cost-effective and reliable alternative to access actionable insights and survive market competition. According to a research report by Canalys, Almost 90% of business partners expected their revenue from managed services to grow in 2023. 56% were confident of over 10% growth in the year. Similarly, 35% of partners said they expected to grow by over 20%. With such statistics, we can be assured that managed analytical solutions are here to stay. Let’s check out what the future holds for data analytics and small businesses. How Does Data Analytics Help Small Businesses? According to a report by NewVantage Partners, 91.9% of businesses achieved measurable results through data and analytics in 2023. The global data analytics market is worth $307.52 billion and is estimated to reach $745.15 billion by 2030 with a CAGR (compound annual growth rate) of 13.5%. Small businesses don’t have the same advantages as established brands and global giants. They need to work harder and smarter to make an impact in the market and attract customers. Data analytics can help create a level playing field and create more opportunities for small businesses to handle uncertainties. Managed analytics insights delivery gives a definite edge over competitors and fortifies the systems and processes in a small business. Additionally, it helps with the following: Adopt Data-Centric Approach A data-centric approach allows the business to make decisions based on reports derived from analyzing historical and present-time data. This strengthens the business position in the market and creates more awareness on multiple levels. The business will be aware of what customer wants, and the target audiences will know about the business’s existence.  Bring Clarity and Transparency Small businesses cannot afford loss due to a lack of transparency or clarity. From finances to market, distribution, and customer service, transparency can reduce the risk of miscommunication and misunderstandings within the organization. Data analytics for small businesses streamlines the workflow, making it easy to track every step of the process. Higher Closing Rates Customers are vital for the survival of a small business. Data analytics helps monitor the customer journey with the establishment and identify areas for improvement. It also provides insights about customer lifetime value so that the business can determine how much to spend on each customer. Better Inventory Management Not having effective inventory management software can lead to many concerns like lack of stock during high demand, leftover stock due to no sales, mismatched orders, delays, and so on. Data analytics optimize the process and establish a clear system to streamline inventory. Businesses can also use analytics to predict market demand and act accordingly. Understand Customer Behavior Customer behavior is a mystery to many brands. However, a business cannot rely on guesswork or historical data to determine what a customer wants. Data analytics effectively combines past and real-time data to provide more accurate insights into customer behavior patterns, thus increasing sales opportunities. Proactive Opportunity Management Analyzing business and market data can uncover many in-depth insights to understand how things stand in the present and what is likely to happen in the near future. Small businesses need to be proactive and grab opportunities before the established brands take over. This is possible by unlocking the power of data and business analytics. Future of Managed Analytics Services for Small Businesses The Analytics as a Service (AaaS) market is expected to touch $68.9 billion by 2028, as per a report by Imarci. Managed Analytics service providers will continue to have a vital role in helping small businesses adopt data analytics to better their offerings. The following are considered some top trends for the future of managed analytics for small businesses: Multi-Cloud Data Systems As per Gartner’s report, cloud computing continues to be the top technology for businesses investing in data and analytics. Small businesses are relying more on cloud solutions to store data, run software, host applications, etc., instead of building an in-house data center that requires extensive maintenance. In fact, small businesses can also use multiple cloud setups to build a stronger data ecosystem. Data-Centric AI Artificial intelligence is no longer limited to large enterprises. Small businesses can effectively leverage AI by investing in a data-centric model as opposed to model-centric or code-centric. It means more small businesses will adopt generative AI to create synthetic data for training the models and deriving better insights. Edge AI Edge AI gives real-time insights as the models analyze the data collected by IoT (Internet of Things) devices. This helps small businesses meet data privacy requirements and reduces the need for complex data storage systems. Increased AI Investment According to a recent poll by Gartner, 2500+ executive leaders said the recent hype around ChatGPT prompted them to increase investments in artificial intelligence. Small businesses can take advantage of the advancements in AI by partnering with managed service providers. What are the Future Trends in Data Analytics? Data Automation Automation is already being used in many businesses at various levels. It will gain more prominence in the future as businesses cannot spend their resources manually processing large datasets. With cloud and embedded analytics offered by managed service providers, data automation will be an in-thing very soon. For example, augmented and real-time analytics require automation and AI technology. Data Governance, Privacy, and Security Business organizations are moving from data warehouses to data lakehouses. A data lakehouse combines the benefits of a data warehouse and a data lake. It provides

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Managed Analytics Services in Healthcare- Improving Patient Outcomes

Digitization of patient records can reduce complexities in providing quality healthcare. Managed analytical service providers play a vital role in assisting hospitals in accessing real-time actionable insights. Here, we’ll discuss how managed analytics can improve patient outcomes. The healthcare industry has been actively adopting the data-driven model in the last few years. The Covid-19 pandemic forced many hospitals and healthcare centers to upgrade their systems and technology. This led to an increasing demand for managed analytics services in the industry. Hospitals are partnering with data analytical companies to deploy and customize analytical models and make effective decisions using real-time insights. According to Mordor Intelligence, the global healthcare analytics market is expected to be $37.83 billion in 2023 and is estimated to reach $106.16 billion in 2028 at a CAGR (compound annual growth rate) of 22.92%. The report also says that the predictive analytics segment will show the most growth during the forecast period. Another report by Global Market Insights shows that financial management, clinical management, and population health management are the three major application areas in the healthcare analytics market.  In this brief guide, we’ll read more about data analytics managed services in healthcare and the need for them in today’s world. What is Managed Analytics Services? Managed analytical services are third-party (near-shore, on-shore, and offshore) solutions provided by data analytical and artificial intelligence companies to help hospitals streamline their workflow, enhance patient care, and improve overall efficiency. It combines different activities or services, such as data warehousing, data analytics, business intelligence, data visualization, big data analytics, data governance, and long-term maintenance. Simply put, the third-party managed analytics companies will deploy a data-driven model in the healthcare center and create custom dashboards to share real-time reports with the staff and doctors. The companies can also provide data maintenance and embedded analytics services where the IT infrastructure is hosted and managed by the service provider. The reports are shared with the hospitals. What is Healthcare Analytics Management? Healthcare analytics is the use of data and statistical analytical techniques and tools to convert raw data into meaningful insights for better decision-making. This data predominantly belongs to the healthcare industry to provide the necessary inputs for hospitals and doctors. Healthcare analytics is a powerful service to unlock the full potential of patient data, discover hidden trends and patterns, identify correlations, and promote patient care. Healthcare Analytics as a Service (HAaaS) has become prominent,+ with more hospitals opting for cloud-based third-party analytical solutions for clinical management. The entire process can be divided into the following: Data intelligence management is a continuous process where new data is constantly added to the database, cleaned, formatted, and analyzed to get actionable insights. It allows healthcare centers to provide premium services to their patients while reducing pressure on doctors. Data mining, building predictive models, and automating reports are some common services in healthcare managed analytics. What are the Three Types of Analytics Used in Healthcare? The term healthcare analytics includes a wide range of processes and solutions, which are broadly categorized into the below three: Clinical Analytics Clinical analytics focuses on patient care. It uses the data from electronic health records (EHR) to predict patients’ future health issues based on their medical condition. For example, hospitals can keep track of patients with critical conditions (heart attacks, cancers, organ dysfunctions, etc.) to know in advance if their situations might take a turn for worse. This allows doctors to provide timely treatment and save the patients’ lives. Financial Analytics Financial analytics deals with the financial impact of treatments on patients. Factors like the cost of the surgery, insurance coverage, cost of medication, etc., are calculated per patient to help them manage their treatments without worrying about the budget. For example, the cost of treatment for a patient with Medicare eligibility will be different from the cost incurred by a patient without such provision. Knowing the overall cost of the treatment will help the patient plan their funds accordingly. Outcomes Research Outcomes research deals with the possible outcomes in the healthcare industry. These include mortality rates, hospital stay duration, re-admission rates, and so on. This data strategy helps the hospital management improve their treatment protocols to become patient-friendly. For example, the hospital beds, ICU rooms, patient monitoring, etc., can be better managed to ensure patients don’t suffer due to the lack of resources. Healthcare data analytics consulting companies set up an automated analytics lifecycle to provide relevant reports to the concerned people at all times. It reduces the risk of mismanagement and miscommunication. What is Data Analytics in Healthcare? Data analytics is not limited to analyzing datasets. It also deals with the overall data framework and how the models are used to extract insights from large volumes of data. In the healthcare industry, real-time analytics insights delivery to decision-makers can promote transparency, efficiency, and cost-effectiveness without compromising quality and patient care. Managed predictive modeling is the process of analyzing historical and present datasets to determine future trends and outcomes. For example, analyzing the behavioral data of patients can help find a way to manage their visit timings, medication, etc. The past and present data of cancer patients can be used to find out if they are at risk of the cancer cells aggravating or relocating in the body to cause more serious trouble. Patients who have previously been admitted for self-harm can be monitored to make sure the risk of repeating the incident is less. Benefits of Healthcare Analytics Applications of Healthcare Data Analytics There are different applications of analytics managed services for health systems. Data analytics are used in the following areas to streamline the hospital’s operational efficiency while saving time and resources.  Strategic Planning Data science managed services are crucial to helping healthcare centers make strategic decisions about treatment facilities, patient records, admission protocols, etc. It helps in tracking the availability of resources, the number of patients waiting, the list of available doctors, potential emergency cases, and so on. The hospital can be ever-ready to handle crises, thus increasing patients’ trust and satisfaction. Staffing Management One of the biggest concerns in the healthcare industry is the lack

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Exploring Future Tech Trends in Data Science Managed Services

Find out how technologies including AI and ML have transformed the future of data science managed services. From tackling the “black-box” problem with Explainable AI to overcoming data governance and privacy concerns, the new technologies can help data scientists deliver value and success in an increasingly data-driven world. Businesses are generating massive amounts of data and there’s no doubt that harnessing the data potential is crucial for staying agile. Managed analytics services have emerged as a game-changer, enabling companies to make data-driven decisions, analyze customer behavior, and drive innovation. As we look ahead to the future of data science managed services, it’s essential to not only understand the emerging technologies and trends but also to foster a better and more professional relationship with our clients. Understanding the Role of Data Science Managed Services The global data science market reached a valuation of USD 95.3 billion in 2021 and is anticipated to increase at a CAGR of 27.7% from 2021 to 2026. Data science managed services have been widely adopted by organizations to access deep expertise and tailored solutions to extract insights from their unstructured data sources. Whether it’s augmented analytics or data management, these specialization services are designed to help clients derive actionable insights from their data, all while saving time and resources. To succeed in this field, it’s crucial to keep pace with the latest developments. Here are some of the emerging technologies and trends that will shape the future of data science managed services. Artificial Intelligence (AI) and Machine Learning (ML) Integration of AI and ML into business processes is becoming increasingly integral to data science-managed services. These technologies can analyze complex data sets, make predictions, and automate decision-making processes. By leveraging AI and ML, data scientists can deliver more accurate and efficient solutions to their clients. Explainable AI (XAI) Tools One of the challenges in AI and ML has been the “black box” problem, where models make predictions without providing insight into their decision-making process. Explainable AI is gaining traction as a trend, as it allows clients to understand and trust the decisions made by machine learning models. Data Governance and Privacy Increased focus on data governance, compliance, and privacy will continue to be crucial concerns within enterprises. Clients expect their data to be handled securely and ethically. Adhering to regulations like GDPR and evolving privacy standards is essential. As data scientists, it’s our responsibility to ensure the data we use is both legal and ethical. DataOps and MLOps DataOps and MLOps are emerging practices that bring DevOps principles to data science and machine learning. These approaches streamline the development, deployment, and maintenance of data science solutions, leading to more efficient and reliable processes. Automated Data Science Automated machine learning (AutoML) and other AI-driven tools are simplifying data science analytics. These tools allow for faster model development and deployment, making data science-managed services more accessible to a wider range of clients. Cloud Computing With data growing exponentially, cloud computing platforms are necessary to store and process this voluminous information. Organizations now have scalable and accessible solutions to store and process data more effectively, enabling quick decision-making through a comprehensive data analysis. The flexibility and scalability offered by cloud-based platforms help organizations to overcome the traditional data management challenges. Internet of Things (IoT) The Internet of Things plays an important role in improving the efficiency and effectiveness of data insights. IoT data analytics make it easy to handle large volumes of data and optimize data collection and decision-making processes to create an adaptable environment conducive to growth. IoT reduces human intervention, facilitating quick processing of raw data and full automation. Also, it offers a more holistic and real-time approach to data analysis, allowing for dynamic adjustments, and thereby improving client outcomes. In a nutshell, IoT ensures optimal business performance by tapping new opportunities, increasing sales, and offering an exceptional customer experience. Conclusion As the future of managed analytics services unfolds, we not only meet with exciting advancements but also new challenges. By embracing emerging technologies like data-as-a-service(DaaS), AI, ML, and Explainable AI, we have the potential to unlock even more value for our clients. However, it is equally important to foster strong professional relationships with our clients. This can be achieved through open communication and collaboration between data scientists, providing personalized solutions, and upholding ethical practices. As the landscape of data science managed services continually evolves, our focus should always be on creating value for our clients and aiding them in thriving in a data-driven world. Fact checked by –Akansha Rani ~ Content Creator & Copy WriterSunaina Meena ~ Digital Marketing Specialist

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Choosing the Right Managed Analytics Service Provider: A Comprehensive Checklist

The blog highlights the importance of data analytics and the increasing demand for analytics service providers. It helps the readers understand the essential factors in choosing the right provider to fulfill data-driven goals and maximize the power of data analytics. Organizations across various industries are continually seeking ways to harness the power of data analytics. Data analytics is at the forefront of business decision-making, helping companies uncover valuable insights, improve operational efficiency, and drive innovation. The present big data analytics market is worth $307.52 billion.  As the demand for data analytics services continues to grow, many businesses are turning to analytics service providers to meet their data-driven goals effectively. This article will guide you through the essential factors to consider when selecting the right provider, ensuring you make an informed decision for your organization’s future success. The Importance of Managed Analytics Services Before delving into the checklist for selecting the right service provider, let’s understand why organizations opt for these services. Managed Analytics service providers are companies specializing in delivering a range of data analytics solutions, offering both expertise and technology. Their primary goal is to help organizations leverage their data to make informed decisions, optimize processes, and gain a competitive edge. Managed Analytics services have become indispensable for businesses of all sizes due to several compelling reasons: Access to Expertise Service providers typically have teams of experienced data scientists, analysts, and engineers who can solve complex problems, design effective solutions, and bring industry best practices to the table. Cost-Efficiency Outsourcing analytics can be more cost-effective than building an in-house analytics team, especially for small and mid-sized businesses. It eliminates the need to hire, train, and retain specialized talent. Scalability Service providers can scale their services up or down based on your organization’s needs, making it easy to adapt to changing requirements without the hassle of recruitment. Focus on Core Competencies By outsourcing analytics, organizations can concentrate on their core operations and strategic initiatives while leaving the data analysis to the experts. Advanced Technologies Service providers often have access to state-of-the-art technologies and tools that may not be affordable for smaller organizations. Now that we’ve established the importance of analytics services, let’s dive into the comprehensive checklist for choosing the right provider. How do you choose the best Managed Analytics Service Provider? Choosing the best managed analytics service provider needs a strategic approach. A thoughtful selection process not only helps you to do the right data analytics but also pushes your business toward data-driven success. Define Your Objectives Before you start looking for an analytics service provider, it’s crucial to clearly define your objectives and what you want to achieve with analytics. Determine your specific business goals, the problems you want to solve, and the outcomes you expect from the services. This clarity will help you find a provider with the right expertise and capabilities. Expertise in Data Analytics and Data Science Look for providers with a strong track record in data analytics and data science. They should have a team of qualified professionals with expertise in machine learning, artificial intelligence, statistical analysis, and domain knowledge relevant to your industry. Check for relevant certifications, case studies, and client testimonials to ensure their proficiency. Range of Services Consider the scope of services offered by the provider. Ensure they cover the entire analytics lifecycle, from data collection and cleaning to model development and deployment. The services should align with your objectives and be adaptable to your changing needs. Data Management and Security Data management is a critical aspect of analytics. The provider should have robust data governance practices, ensuring the quality, security, and compliance of your data. Inquire about their data security measures, adherence to industry regulations, and disaster recovery plans in case of data breaches. Technology Stack Evaluate the technology stack used by the provider. They should be up-to-date with the latest analytics tools, platforms, and frameworks. Compatibility with your existing systems is essential to ensure seamless integration. Scalability Consider the provider’s ability to scale their services as your organization grows. They should have the flexibility to accommodate increased data volumes, expanded user base, and evolving analytics needs without disrupting your operations. Industry Experience Assess whether the provider has experience in your specific industry. Industry expertise is valuable, as it ensures they understand your unique challenges, regulatory requirements, and business dynamics. Customization Each organization has its own set of needs and preferences. A good service provider should offer customized solutions that align with your specific requirements. Avoid one-size-fits-all approaches. Data Intelligence Look for providers that not only analyze data but also offer data intelligence services. This involves extracting meaningful insights, identifying trends, and making proactive recommendations to drive strategic decisions. Data Visualization Effective communication of insights is crucial. Ensure the provider can create compelling data visualizations and dashboards that make complex data accessible to non-technical stakeholders. Monitoring and Support Check the provider’s ability to monitor analytics processes in real-time and provide support when issues arise. Service level agreements (SLAs) should be clear and aligned with your business goals. Cost Structure Understand the provider’s pricing model, whether it’s subscription-based, pay-as-you-go, or project-based. Transparent pricing will help you budget effectively and avoid unexpected costs. Case Studies and References Ask for case studies and client references to get insights into the provider’s past projects, success stories, and client satisfaction levels. This can help you gauge their capabilities and reliability. Performance Metrics Define key performance indicators (KPIs) that will measure the success of your analytics initiatives. Discuss these KPIs with the provider and ensure they align with your business objectives. Cultural Fit Lastly, assess whether there’s a cultural fit between your organization and the service provider. Effective collaboration and a shared vision are essential for a successful partnership. Conclusion In conclusion, choosing the right managed analytics service provider is a pivotal decision for your organization’s data-driven journey. By following this comprehensive checklist, you can select a provider that aligns with your business objectives, offers the expertise and technology required, and provides the necessary flexibility

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The Ultimate Guide to Managed Analytics Services: Benefits and Best Practices

Organizations today are swimming in a sea of information. To make sense of this data and extract valuable insights, many companies are turning to data Managed Analytics Services. In this blog, we’ll take a deep dive into a guide to managed analytics services and help you determine if they’re a good fit for your organization. The world of data analytics is booming, with the global data analytics market projected to surge from USD 43.0 billion in 2020 to a staggering USD 167.3 billion by 2025, at a CAGR of 28.4% during this period. Organizations today are increasingly turning to data managed analytics services to make the most of their data. These services are like a guiding light, helping businesses harness the full potential of their data resources. In this comprehensive guide, we will dive into what data managed analytics services entail, explore the wide range of services they offer, highlight the benefits of embracing them, and outline best practices for forging partnerships with managed analytics services providers. What are managed analytics services? Data managed analytics services include a wide range of activities that empower businesses to extract valuable insights from their data, make informed decisions, and gain a competitive edge in their respective industries. These services can be categorized into various components: Data Warehousing Data warehousing involves the collection, storage, and retrieval of structured data from various sources. Managed services providers assist in designing, deploying, and maintaining data warehouses, ensuring that businesses can access data rapidly and with minimal downtime. Data Governance Data governance is the practice of managing data assets effectively. It involves defining policies, standards, and procedures for data management, ensuring data quality and security, and adhering to regulatory compliance. Big Data Analytics Big data analytics involves setting up and managing big data infrastructure, helping organizations process and analyze large datasets efficiently. Data Architecture Data architecture is the blueprint that defines how data is stored, organized, and accessed within an organization. It involves creating and managing data architectures that align with the organizational goals, allowing seamless data integration and analysis. Data Lake A data lake is a centralized repository for storing both structured and unstructured data. Designing and managing data lakes makes it easier for organizations to ingest, store, and analyze diverse data types without the constraints of a predefined schema. Benefits of Leveraging Data Managed Analytics Services Now that we’ve outlined what data managed analytics services offer, let’s delve into the benefits of partnering with a managed analytics services provider: Deep Expertise Managed analytics services bring in experts who have a profound understanding of data analytics, data science, and related technologies. Their knowledge keeps your organization updated with the latest trends and best practices. Focus on What Matters By delegating data analytics tasks to a managed services partner, organizations free up their in-house resources to concentrate on core business activities, boosting overall productivity. Data Security and Compliance Security and compliance are top priorities in the data analytics landscape. Managed service providers implement measures to safeguard data privacy, adhere to industry regulations, and protect against security threats, providing organizations with peace of mind. Accelerated Insights Leveraging managed analytics services speeds up the process of deriving meaningful insights from data. This accelerated insight generation can lead to faster decision-making and a competitive edge in the market. Best Practices for Partnering with Managed Analytics Services Providers To make the most of your collaboration with a managed analytics services partner, consider the following best practices: Define Clear Objectives Clearly define your business goals and the specific outcomes you expect from your partnership with a managed services provider. Having a well-defined scope will guide the service provider in delivering the services you need. Select the Right Partner Choose a managed services provider that aligns with your industry, understands your data-related challenges, and has a proven track record. Look for providers with expertise in your specific domain. Open Channel of Communication Effective communication is essential. Maintain an open and transparent channel of communication with your service provider to ensure they understand your evolving needs and can make adjustments accordingly. Data Access and Ownership Clarify data access and ownership agreements. Ensure you have control over your data and that it is used by your organization’s policies. Service-Level Agreements (SLAs) Establish SLAs that clearly define the service levels, response times, and performance metrics. SLAs help ensure that your provider delivers services consistently and reliably. Security and Compliance Verify that your provider follows robust security practices and complies with relevant regulations. Data security should be a top priority, and the provider should have appropriate safeguards in place. Continuous Monitoring Regularly assess the performance of your managed services provider and provide feedback for continuous improvement. This ensures that the partnership remains beneficial over time. In Conclusion In an era where data is often hailed as the new currency, data managed analytics services offer a strategic avenue for organizations to harness the power of data analytics and data science. By partnering with the right managed services provider, businesses can gain access to expertise, reduce costs, improve data security, and speed up the time-to-insights. Furthermore, adhering to best practices in your partnership ensures a successful and mutually beneficial collaboration. Embrace the advancements of data managed analytics services, and let your data work for you. Fact checked by –Akansha Rani ~ Content Creator & Copy WriterSunaina Meena ~ Digital Marketing Specialist

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