Data Engineering Services: A Modern Business Essential

Data engineering focuses on the practical applications required to establish a flexible, scalable, and agile data infrastructure in an enterprise. It is the most vital part of adopting the data-driven model to make business decisions. Here, we’ll discuss data engineering services and their role in modern businesses.  Data is an integral part of the IT industry. Over the years, users have generated large volumes of data, which is being collected by businesses to fine-tune their products/ services and enhance customer experience. Statistics show that an average user generates 1.7 MB of data per second.  Around 97.2% of businesses are investing in artificial intelligence and big data, though a study shows that many companies analyze only up to 40% of the data they collect from all sources. Another interesting observation is that companies that actively use big data analytics increase their profits by around 8%. Wouldn’t it be beneficial for businesses to further utilize data to amplify their profits?  So, how can it be done?  The answer is big data engineering services.  In simple terms, data engineering is the process of streamlining data collection, storage, and analytics to get more insights from datasets. However, it is an elaborate process that requires IT infrastructure and expert skills. Data engineering is the foundation of building the data analytical model in an enterprise. Businesses partner with data engineering companies to adopt the data-driven model for effective and faster decision-making. Enterprises find it a cost-effective solution to rely on offshore data engineering service providers to derive actionable insights using AI and big data analytics.  In this blog, we understand the process in detail and explain why data engineering is needed for every modern business around the world.  What is Data Engineering? Data engineering is the process of collecting and validating data to ensure high-quality datasets are available for data scientists. Data engineering is a vast domain that includes a range of skills, tools, and applications. It is a combination of several modules like data infrastructure, data mining, data crunching, data acquisition, data modeling, and data management.  A data engineer should maintain the data infrastructure that supports business intelligence solutions. They should work with programming languages, database software, machine learning, and artificial intelligence algorithms. They can work in small teams that focus only on ingesting data into systems or be a part of large teams offering data engineering services & solutions and collaborate with data scientists and database administrators to streamline the data pipeline in mid and large-sized enterprises.  What are Data Engineering Services? Data engineering services are varied and versatile. The top data engineering services companies offer end-to-end solutions to design, build, deploy, and maintain a seamless system that collects, cleans, stores, processes, analyzes, and visualizes data through BI tools. The following are some important services offered by the companies:  Data Ingestion  Data ingestion is the process of moving or replicating data from sources to the cloud storage platform. It is a prominent step in the modern data stack. It determines the quality and type of data a business uses for analytics. Data engineers have to determine if this process will take place in batch mode or in real-time. Factors like cost and resource allocation play a vital role in finalizing the time frame for data ingestion.  Data Storage  Data storage management is another crucial part of data engineering services. The data collected from multiple internal and external sources has to be stored in a central database for further processing and analysis. Data engineers have to design the best data storage method that allows employees to access datasets in real-time. Data storage solutions can be on-premises or on the cloud. Businesses can even use a combination of both. Data warehousing and data lakes are two popular methods used to store vast amounts of data. Businesses offer Azure data engineering and AWS data engineering services to build and customize cloud data storage centers.  Data Integration  Data integration is a key data engineering service as it sets up the necessary connections between different systems, apps, and databases. It is the process of setting up the connection between the central database with the input and output channels. For example, the sources have to be connected with the data warehouse to collect data. Similarly, the data warehouse has to be connected with ERP systems and BI tools to run analytics and share data visualizations with the end user.  Data Processing Data processing is the process where large datasets are cleaned and manipulated to derive useful information. Data from the data warehouse or data lakes are retrieved, classified, cleaned, and formatted to make it ready for analysis. This stage helps remove errors and duplicate data to increase the accuracy of the derived insights.  It is yet another important part of data engineering services as low-quality data can result in incorrect insights which can lead to wrong business decisions.  Business Intelligence  Business intelligence is a vital part of the process. This is where data is converted into meaningful information and presented in graphical reports. Data engineers have the responsibility to identify the right BI tool based on business requirements and customize it accordingly. The dashboards also have to be set up and integrated with the rest of the infrastructure to provide data visualizations in real-time to employees across all departments.  How Does Data Engineering Help a Business? Data engineering or information engineering is the foundation for adopting and using the data-driven model in an enterprise. Data engineering and analytics go hand in hand and have to be aligned at all times to ensure that the top management and employees can access actionable insights at any given point in time. This allows them to make faster decisions based on reliable reports rather than guesswork.  Once data engineers set up the data architecture (systems and connections), data scientists can perform the analytics and share reports. Artificial intelligence tools and machine learning algorithms are used in the process to ensure the seamless and real-time flow of data from one system to another.  Typically, data and engineering services help businesses in the following ways: Data engineering companies also offer data analytical

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Six Big Losses in OEE and Four Tips to Improve Them: From Inefficiency to Excellence

OEE is Overall Equipment Effectiveness, a standard measurement to determine the efficiency and productivity of a manufacturing industry. Here, we’ll discuss ways to resolve the six big losses in OEE and the need for Power BI services to achieve the results.  The manufacturing industry has been growing despite various concerns due to supply chain disruptions, labor shortages, inflation, and restrictions that affected the global market in the last couple of years. According to Statista, global industry production (excluding the US) increased by 1.53% in December 2022. The emerging markets registered an increase of 3.3%, while US industrial production increased by 2.07% for the same period.  Deloitte released a report detailing the top five trends to watch out for in the manufacturing industry. Investing in technology to mitigate risk is the first and most important trend for 2023. This is an important factor as many manufacturers and working towards OEE improvement in production.  The six big losses in OEE (Overall Equipment Effectiveness) can severely affect the revenue and growth of a manufacturing enterprise. Technology plays a vital role in minimizing OEE loss and increasing the overall quality, production, and profits of the industry. Manufacturing companies partner with business intelligence service providers to digitally transform their processes and improve OEE effectively. In this blog, we’ll read more about what OEE is, the losses of OEE, and ways to resolve them in a business.  What is an OEE Breakdown?           OEE (Overall Equipment Effectiveness) is considered the gold standard to measure manufacturing productivity. A business with higher OEE implies being more productive and optimally using resources, while reducing defects or wastage. Quality, performance, and time are the three major parameters used to measure OEE.  Measuring OEE in manufacturing helps the industry improve its processes, benchmark progress, and increase the productivity of equipment by eliminating waste. Typically, a business with 100% OEE is termed perfect, while a business with 85% OEE (or more) falls into the world-class category. A typical industry will have about 60% OEE, and anything close to 40% or less is considered low OEE. This denotes that the manufacturer has a greater share of losses and should address the issues to get better results.  OEE = (Good Count × Ideal Cycle Time) / Planned Production Time Availability Planned Stops Unplanned Stops  Performance Small Stops Slow Cycles  Quality Production Rejects Startup Rejects  Overall Equipment Effectiveness (OEE) Fully Productive Time  What are the Six Big Losses in OEE? Before we learn how to improve OEE, let’s first understand the six big losses that affect the manufacturing industry.  1. Equipment Breakdowns  Equipment breakdowns and failure account for significant loss of time and productivity in an enterprise. This is usually categorized as availability loss as it is unplanned downtime. The equipment is not running when it is expected to run leads to wastage of time (puts everything behind schedule) and productivity (the factory produces a lesser quantity of goods due to equipment failure or breakdown).  This loss can also include relevant aspects such as tooling failure, unplanned maintenance, lack of material or operator (worker), or due to a blockage on either side of the manufacturing process. For example, if the production of a certain product involves eight steps, a disruption at the third step will affect the fourth step just as the disruption at the fifth step.  2. Machine Setup and Adjustment  The second loss of OEE in production deals with the lack of availability of the equipment due to setup, installation, customization, or other adjustment requirements. This is the duration where the machine has to be running but is instead being adjusted (due to various reasons).  Though it is also considered availability loss, it comes under planned stops (as the workers or technicians stop work because they need to adjust the equipment before it starts running again). Cleaning, periodic maintenance, warmup time, cool down time, quality inspections, etc., are some examples of this.  3. Minor Stops or Breaks  Minor stops or idling time is the short duration when the machine’s operator stops its usage to resolve an issue. This is usually a minute or two (for each stop). It is categorized as performance loss because the equipment is available and running but is not performing (or producing) during that short period.  Minor stops can include machine idling due to a slight delay in feeding input material, incorrect settings, misaligned sensors, material jams, design complexities, and periodic mandatory cleaning sessions. All kinds of stops or breaks (in production) that are for less than five minutes for each period come under this type of OEE loss. These can be hard to track and can lead to major breakdowns if ignored.  4. Slower or Reduced Speeds This is the duration when the equipment is slower than the Ideal Cycle Time, thus resulting in the production of fewer goods than expected. It is categorized as performance loss as the equipment is not producing as many goods as it usually does. The Ideal Cycle Time is the fastest possible time taken to manufacture a product. It is a theoretical value calculated based on the given specifications of the machine.  Many reasons can lead to reduced speeds. A few common causes are improper equipment maintenance, poor lubrication, jamming, worn-out spare parts, bad working environment (unsuitable for production), poor quality raw or input materials, inexperienced operator, sudden shutdowns, etc.  5. Process Defects  Every batch produced may have some defective parts or goods despite the production process being stable throughout. Both scrapped products and the ones that can be reworked or reused are totaled in this loss. OEE measures quality based on the First-Pass Yield, which makes process defects a quality loss issue. If the quality of the products is good, they wouldn’t be marked defective.  Process defects can be due to various reasons like wrong or incorrect settings, providing wrong raw materials to the equipment, the inefficiency of the operator, or the expiration of materials (commonly found in pharmaceutical and food industries).  6. Lesser Yield or Higher Scrap The last OEE loss is reduced or lesser

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9 Top Business Intelligence Trends for 2023 (Updated)

The business intelligence landscape has been completely revolutionized over the past ten years, and access to the cloud has become a common phenomenon. Interactive business dashboards and insightful data visualizations replaced plain spreadsheets, and the use of data exploded. The past few years saw a massive boom in the business intelligence industry. The data product chain became democratized due to the rise of self-service analytics. Data began to be handled not just by analysts. Some of the trends continue to be a part of the business intelligence industry. But in the current year, the strategies and tools have become even more personalized, and some new trends have emerged too. Businesses- small or big, are no longer contemplating the need for business intelligence analytics but are rather concerned about which BI solution fits the best within their business needs. Every business is now aware that they need data visualizations for better analysis. So they are busy figuring out how to leverage modern BI dashboard software to present data stories in the best possible manner. The theme of business intelligence this year will revolve around data discovery and data security: clean and secure data presented but powerfully. There will be collaborations between artificial intelligence and business intelligence.  Top Business Intelligence Trends Some of the biggest business intelligence trends are a no-brainer. Artificial intelligence leads the race with workforce automation and digital transformation sectors seeing massive improvements. Read on to know why. Artificial Intelligence Artificial intelligence aims to make machines perform those complex tasks on their own that can only be executed through human intelligence. Our interactions with analytics and data management are getting revolutionized through artificial intelligence. According to the Strategic Technology Trends report, the trend will combine engineering and hyper-automation with AI with a high focus on possible security risks and vulnerable attack points. Businesses can enjoy real-time alerts about what is happening every second and get immediately notified about unexpected events. Integrating AI in BI solutions will assist in automatic and comprehensive analysis of the full dataset from any data source without human effort. You can instantly access business reports on growth or trends or forecast, anomalies, what-if analysis, value drivers, key segments correlations, etc. AI can also be utilized for online verification processes, like CAPTCHA technology, with the help of generative adversarial networks (GANs). Data Discovery Data discovery means discovering patterns and discrepancies in data. It is the process of using advanced analytics and visualizations to present all the data collected from different internal and external sources. It has great benefits in keeping relevant stakeholders involved with the data since it allows them to extract actionable insights and intuitively manipulate and analyze the data. The demand for data discovery tools across businesses of all sizes has boomed due to the increasing need for data usage and insights. Generating insights that add value to the business requires a deep understanding of the relationship between data in the form of guided, advanced analytics, visual analysis, and data preparation.  Online data visualization and discovery tools are helping businesses create a sustainable decision-making process. The detailed and interactive reports or sales charts presented with several graphs will help teams spot crucial outliers and trends within minutes. Since it is a fact that humans process visual data better, in 2022, the usage of the dashboard as a visual communication and collaboration tool will increase.  In-depth data analysis through interactivity and augmented analytics will replace simple KPI monitoring. KPI dashboards will have other interactive features, too, such as real-time data and AI-based alarms. Business users need software for this purpose that is: Data Literacy The ability of businesses to use data analytics and insights in their decision-making has become a core factor in determining the business’s success. From goal setting to strategizing to taking action, businesses require data at every step. No wonder data literacy is of utmost significance for every business. It is the reading, writing, analyzing, and communicating data in a particular context. Data literacy requires a deep understanding of all the tools and technologies adopted and techniques and methods implemented for data analysis.  Business leaders must equip all the organization members with the training and tools required for working with data and analytics. Managers need to assess the skill sets of employees, and managers need to identify gaps and weak spots. Team members fluent in data can be appointed as mediators for non-skilled groups. With the right tools and quality training, all the members will acquire enough data literacy to use data as the key language and perform advanced analysis. By 2025, prediction says that data literacy will be so widespread that businesses will no longer require data scientists to progress technologically. Data Quality Management Currently, there is abundant data in every business flowing in from literally everywhere, and it has become crucial to assess data quality before using it. Poor quality data can cost businesses around $9.7 and $14.2 million per year. No wonder data quality management is an increasingly significant trend. Poor data quality can lead to a poor understanding of consumer behavior, wrong estimation of conversion rates, poorly generated marketing budgets, incorrect resource allocation, bad investments, and other errors that can harm businesses significantly.  Data quality management is the solution to all these problems. It ensures that businesses only use the correct data for analytical purposes to arrive at the right data-driven decisions. Data quality depends upon how complete, timely, accurate, consistent, and compliant it is. There can be no outdated data that does not fit within the timeline or duplicate or missing values. Companies are collecting complex data from several sources regularly, and managing these data using the right tools and processes has become critical. Predictive & Prescriptive Analytics Tools Predictive analysis means forecasting future possibilities by extracting information from existing data sets. It is data mining of past data. Companies get an insight into their future along with alternative scenarios and risk assessments that are reliable enough. It helps companies better understand their customers, products, and

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Breaking Down Language Barriers: How AI is Revolutionizing Translation

In today’s world, the ability to communicate effectively across multiple languages has become more important than ever. As businesses continue to expand their global reach, the demand for high-quality translation services has increased significantly. Fortunately, advancements in artificial intelligence (AI) have led to the development of sophisticated translation technologies that are transforming how we communicate across different languages. Here, we will explore how AI is revolutionizing translation, the benefits of using the latest AI powered translation technologies, and the potential impact on the future of communication. The Evolution of Translation Technology Translation technology has come a long way. Initially, translation was done manually, with human translators spending hours poring over texts and dictionaries to translate them from one language to another. However, this method was slow and expensive and often resulted in inaccuracies due to the human element involved. With the advent of computers, machine translation was introduced in the 1950s. Machine translation uses software to translate text automatically without human intervention. However, the quality of the translations produced by machine translation was often poor due to the limitations of the software at the time. In recent years, advancements in Artificial Intelligence and machine learning have led to the development of sophisticated translation technologies that are capable of producing high-quality translations in real-time. These technologies are changing how we communicate across different languages, making it easier than ever to break down language barriers. How AI Translates Languages AI powered translation tools use machine learning algorithms to analyze vast amounts of language data and learn how to translate different languages accurately. These tools can process natural language and identify patterns and relationships between different words and phrases. By doing so, they can translate text from one language to another with impressive accuracy. Types of AI Powered Translation Tools There are several types of AI powered translation tools, each with strengths and weaknesses. Some of the most common types include: Rule-Based Machine Translation Rule-based machine translation involves translating text based on a set of predefined rules. These rules are created by human linguists and programmers, and the translation process involves matching words and phrases in the source language to their corresponding words and phrases in the target language. Statistical Machine Translation Statistical machine translation involves analyzing vast amounts of language data to identify patterns and relationships between different words and phrases. By doing so, statistical machine translation tools can translate text from one language to another with high levels of accuracy. Neural Machine Translation Neural machine translation involves using deep learning algorithms to process natural language and translate text from one language to another. These algorithms can learn from vast amounts of language data, making them highly accurate and efficient. The Benefits of AI-Powered Translation AI powered translation offers several benefits over traditional translation services, including: Cost-Effectiveness AI powered translation tools are often more cost-effective than human translators, making them an excellent option for individuals and businesses on a budget. Efficiency AI powered translation tools can translate vast amounts of text quickly, making them an excellent option for individuals and businesses that need to translate large volumes of text regularly. Accuracy AI powered translation tools can translate text with impressive accuracy, making them an excellent option for individuals and businesses that require high levels of accuracy in their translations. Implications for Businesses and Individuals The rise of AI powered translation has significant implications for businesses and individuals alike. For businesses, AI translation tools offer a cost-effective and efficient way to communicate with customers, employees, and partners in different countries and linguistic regions. This can help to expand the reach of businesses and increase their global competitiveness. For individuals, AI translation tools can make it easier to navigate daily life in a new country, communicate with locals, and access information in a different language. Challenges and Limitations of AI Translation While AI powered translation tools offer many benefits, there are also several challenges and limitations to consider. First and foremost, these tools may struggle with idiomatic expressions, cultural nuances, and other subtleties of language that human translators are more adept at handling. Additionally, AI translation tools may not be suitable for translating highly technical or specialized content, as they may not have the required expertise or knowledge. Finally, there is a risk that reliance on AI translation tools could lead to a loss of human language skills and cultural knowledge over time. The Future of Communication The potential impact of AI powered translation technologies on the future of communication is enormous. As these technologies continue to improve, they will make it easier than ever for people to communicate across different languages, breaking down language barriers and promoting cultural exchange. In the future, we may see AI powered translation technologies integrated into everyday devices like smartphones and laptops. This would make it possible for people to communicate with each other in real time, regardless of the language they speak. The impact of AI powered translation on businesses is also significant. As businesses continue to expand globally, the ability to communicate effectively with customers in different languages will become increasingly important. By using AI powered translation technologies, businesses can communicate with their customers in real-time, regardless of the language they speak. Conclusion In conclusion, advancements in artificial intelligence have revolutionized translation technology, making it easier to communicate effectively across multiple languages. AI powered translation tools use machine learning algorithms to analyze language data and identify patterns, resulting in accurate translations. There are several types of AI powered translation tools, each with strengths and weaknesses. They offer cost-effectiveness, efficiency, and accuracy over traditional translation services. As AI powered translation technology continues to improve, it has the potential to break down language barriers and promote cultural exchange, making it easier for people to communicate across different languages in real time. This technology also has significant implications for businesses looking to expand globally and communicate with customers in different languages. Originally Published on The Tech Trend

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How to Choose the Right Affiliate Program: Partner Up For Success

Affiliate programs let you monetize your web traffic and earn a commission for promoting a product or brand. It offers flexible and versatile earning options for website owners, entrepreneurs, and freelancers. Here, we’ll discuss the ways to choose the right affiliate program to generate more revenue.  Affiliate programs have grown by leaps and bounds over the years. It started as a side hustle and has become a prominent income source for millions of people from around the globe. Brands have expanded into the affiliate market so much that 81% of businesses use these programs to enter new markets and increase their customer base. Global affiliate program is valued at more than $17 billion. In fact, it is a major income generation source for 31% of publishers (website owners).  But what is an affiliate program?  An affiliate program is a mutual exchange program between a brand and a publisher, where the website owner (publisher) gets rewards for posting about a brand, referring and providing links to its products or services. It is a performance-based marketing channel that provides returns based on website traffic, click rate, subscription and purchase frequency.  For example, DataToBiz’s affiliate program offers exciting opportunities like an instant credit of $10 for generating a qualified lead and a flat 10% commission for each sales conversion. The company also offers recurring commissions and other incentives.  How Do Affiliate Programs Work? An affiliate program has four major parties involved in the process: The working of an affiliate business program can be complex for newbies. However, it is a structured and well-established process followed by several businesses. Some variations occur depending on how the businesses plan the commission model, but the overall process is pretty much the same.  This commission amount will not be collected from the customer. It will be paid for by the company. Brands follow different payment models or structures based on various factors like the type of affiliate network, the number of affiliates, industry regulations, etc. However, the following are the three commonly used payment structures:  Over the years, affiliate companies have developed unique and attractive commission schemes to add more affiliates to their network and promote the brand. These include: How to Find Affiliate Products? Google Trends shows that the search for ‘affiliate program’ doubled between 2015 and 2021. Moreover, 56% of advertisers and agencies have earned more revenue from affiliate programs since the Covid-19 lockdown in March 2020.  But how to find the right affiliate products to start growing your career as an affiliate marketer? Let’s find out.  Google Search  Do a quick search on Google to see a list of the top products trending in the searches. Get data from Google Search Console and analyze it to shortlist products.  Competitor Analysis How are your competitors in your niche? How many of them are affiliated with different businesses? Make a list of such publishers and the brands they promote. This will give you an idea about which products have more affiliates. However, remember that their products may not always be suitable for your audience.  Social Media Trends  First, identify the channel where your target audience is active. Then go through the ads, posts, and hashtags on the platform to identify which products are currently hot sellers. Also, gather information about products they are struggling with so that you can provide a solution o an alternative.  Google Trends  Google Trends is a free tool that helps you collect data about the latest search trends on the internet. You can get data based on geographical locations, volume, duration, etc. Make note of recurring products and add them to your shortlist.  Audience Feedback  Conduct polls and surveys on your website and social media accounts to ask for the opinion of your audiences. You will have to analyze the feedback to gather the necessary insights. Ask genuine and open-ended questions to get a better response.  Research Online Categories  The product categories listed on marketplaces like Amazon, eBay, etc., can provide an idea about the range of options available in your niche. You can go through blogs and articles that list online tools if you belong to the IT industry.  Products You Used  The easiest way to finalize products for an affiliate program is to choose from the ones you used. When you promote products you like, it becomes easier to convince others with your posts and experiences.  Affiliate Directories Check out the leading affiliate directories and networks in the market. Sort the products based on categories, commission rates, brand image, etc. It is not always a good idea to choose affiliate products with a higher commission. Other factors should also be considered before you make the final decision.  How to Choose the Right Affiliate Program Choosing the right affiliate program requires time, energy, and research. You need to be 100% sure of the brand and products you want to promote as an affiliate.  Niche Compatibility  The most important aspect is the niche and industry you belong to. It makes sense to choose businesses from your industry or relevant ones instead of something totally different. For example, a food blog specializing in baking and cake decorations should choose an affiliate program to sell baking equipment, electronics, gadgets, and items used to decorate baked goods.  Similarly, a website that explains artificial intelligence-based tools used in EdTech and training will find it beneficial to become an affiliate partner to an AI company from the niche. You can also promote other tools by the company as they are likely to be AI-based.  Another example is where a teacher can become an affiliate to sell online assessment tools like PrepAI, which allows students and their parents to create their own tests for practice.  Products Offered  Go through the lists of products available in your niche and the adjacent ones. You can start with a wider net and narrow it down as you get closer to finalizing the affiliate program. Here, many factors like product type, price, usage, limitations, etc., should be considered.  While some affiliates prefer to promote high-priced products, others opt for affordable products and work on attracting

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Ankush Sharma on How AI is Revolutionizing Hiring – CEO Blindspots Podcast

🎧 Episode Overview In the latest episode of CEO Blindspots with Birgit Kamps, Ankush Sharma, the CEO of DataToBiz, shared his experience of developing and using HireLakeAI to successfully recruit and hire a talented team of professionals who contributed to the company’s recognition as one of the “Top 1000 fastest-growing firms”. Ankush explained how AI can streamline the recruitment process and help in selecting the most suitable candidates for each job role. Join Ankush Sharma as he shares the origin story of HireLakeAI and the efforts he and his co-founder, Parindsheel S Dhillon, put in during its initial phase. Discover how the COVID-19 pandemic impacted recruiting and HireLakeAI emerged as an innovative solution.  Tune in to the podcast! 💎 Key Takeaways 💬 Key Quotes “The DNA of a company is the reflection of the DNA of its founders.” “We do not believe in the culture of, hiring and firing.” 💡 To Sum Up! Join us in this episode as we explore how solving an internal challenge led to the creation of a game-changing solution for revolutionizing the hiring process. Discover the story behind HireLakeAI and the innovative ways it’s transforming the hiring culture.

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How to Set Up Power BI Sales Dashboard – Effortlessly Visualize Sales Data

A sales analytical dashboard will empower you to gain an edge over competitors and increase profits for your business. Here, we’ll discuss the steps to create a sales dashboard in Power BI.  The sales team has to don many hats. From creating marketing strategies to directly interacting with end users, the sales department is burdened with a heavy workload. Sales analytics helps the teams stay up to date and be proactive in handling leads to increase revenue.  Investing in business intelligence tools provides the department with the necessary technical support to be one step ahead of competitors.  According to Finances Online, 90% of sales and marketing teams mention that BI tools increase their work efficiency Similarly, a business intelligence sales dashboard is the best tool to empower sales reps and help them make the right decisions.  Microsoft Power BI is among the most used business intelligence tools in the global market. Setting up a Power BI sales dashboard and customizing it for the business will ensure that the department can monitor leads, customers, revenue, expenses, profits, and all other KPIs (key performance indicators) in real-time. You can achieve this by partnering with offshore BI service providers.  In this blog, we’ll discuss the steps to set up a Sales Power BI Dashboard for the team. We will also look at the reasons to hire offshore Power BI experts instead of building an expensive in-house team from scratch.  What is Microsoft Power BI? Power BI is a suite of applications developed by Microsoft to process large amounts of data and analyze it in real time. The insights are presented on the interactive dashboards through data visualization. The graphical reports are easy to read and used for day-to-day decision-making. Power BI is a cloud-based business intelligence tool that can be adopted in different departments of an enterprise.  What Goes into a Sales Dashboard? A sales dashboard is an interface that provides actionable insights by processing data from CRM systems. Building a BI sales dashboard can be complex. You should correctly identify the KPIs to track and goals to achieve.  Before you decide to set up a weekly sales dashboard, you need to consider the following: A few important metrics or KPIs to include in your sales dashboard are: The effectiveness of reports depends on how accurate your KPIs are and how well you customize the Power BI sales dashboard to align with your business goals.  How to Set Up Power BI Sales Dashboard? Setting up a Power BI sales dashboard involves eight steps. The process can be simple or lengthy, depending on the data volume, sources, dashboard layout, and KPIs.  1. Create Your Power BI Account  The first step is to create a Power BI account. You can start for free by clicking on the Try Free option on the Microsoft Power BI website. Provide your official email ID or Microsoft 365 login details if you already have an account. Complete your profile and follow the instructions on the screen to set up your profile.  2. Import Data from Sources  The next step is to import the data required to create dashboards. You can upload Excel or CSV files (exported from other platforms or apps). Microsoft Power BI accepts different types of input files.  You can also integrate Power BI with databases like Salesforce, Dynamics, SharePoint, Azure, etc. Power Bi is compatible with several CRM systems and third-party databases. You can set up API integrations for real-time data flow between the platforms. In fact, you can get data from multiple sources simultaneously.  3. Import Datasets with Report  Once you complete the data importing step, you should import Power BI datasets to create the dashboards. Power BI adds a dataset and reports when you import data to the platform. The reports are automatically generated using PowerView sheets.  4. Select Tables to Generate Reports  The tables in the imported data can be automatically converted to visualization reports. Go to Navigator View and click on the Edit option. Go to Query editor to add and filter the data in tables to create the reports.  5. Define Relationships Between Data Sources  By defining the relationships between different tables, you can create custom visualization to accurately monitor every one of the sales KPIs. You can create a Power BI revenue dashboard by following the same steps.  Select Foreign Keys to establish the relationships between two or more tables (data sources). Take time to correctly assign the keys and define relationships and click Ok when done. You can go back to the Report View through the tab on the top left.  6. Customize Visualization Elements  So, how do you want to display sales data? The choice is yours. You can customize the interface to suit the sales teams’ requirements.  Microsoft Power BI offers great visualization to present reports in graphical formats. You can choose the types of graphs, charts, etc., you want to see on the sales dashboard. Drag and drop the necessary elements onto the dashboard. You can also move them around and adjust their size, positions, filters, etc.  7. Dashboard Embedding  For third-party integrations, enter the login details to your database account. Follow the instructions on the screen to configure the database. The reports are ready to be embedded in other platforms.  8. Create Power BI Application  This step is optional. You can register an application with Microsoft Power BI to get the application ID and code generated to create the app.  Why Hiring Business Intelligence Consulting Companies is a Smart Move  Setting up the Power BI sales dashboards requires expertise. The talent  gap is a real issue in many enterprises. You can hire new talent, train existing employees, or opt for offshore partners to handle the project. By hiring a BI consulting company, you get the following benefits: Greater Industry Experience  BI service providers work with different businesses and have deeper knowledge about the process of setting up Power BI dashboards for sales. For example, DataToBiz has clients from various industries, ranging from manufacturing to healthcare, IT, retail, supply chain, eCommerce, etc., and works with MSMEs,

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Complete Guide to Power BI Sales Dashboard – Gain Better Sales Insights

Sales analytics is an essential component of any successful business strategy, as it allows organizations to track and analyze their sales performance data to identify areas for improvement and growth. However, many sales teams still rely on traditional methods to track their performance, such as spreadsheets and manual data entry. This often leads to incomplete or inaccurate data management and analysis. Without a data visualization solution to display their sales data in an intuitive and easy-to-understand format, sales teams can miss out on critical insights that could improve their performance. By implementing a data visualization solution, organizations can empower their sales teams to make data-driven decisions that will drive revenue growth and improve overall performance. Power BI is a business analytics service by Microsoft that provides interactive visualizations and business intelligence capabilities with an interface simple enough for end-users to create their reports and dashboards. With Power BI, businesses can analyze data from a variety of sources and gain valuable insights to make informed decisions. What are Power BI Sales Dashboards? Sales dashboards are one of the most important applications of Power BI for businesses. These dashboards enable companies to track their sales performance in real-time, identify trends, and make data-driven decisions to improve sales effectiveness. In today’s data-driven business environment, BI sales dashboards are an essential tool for businesses to stay competitive and profitable.  Why use Power BI Sales Dashboard to Gain Better Sales Insights? Sales dashboards are an essential tool for businesses to gain more control over their sales performance. Here are some of the reasons why businesses should use Power BI sales dashboard to gain better sales insights: Real-Time Data Analysis With Power BI, businesses can track their sales performance in real-time and gain insights into their sales trends, customer behavior, and product performance. Real-time data analysis enables businesses to make quick decisions and respond to changes in the market promptly. Customizable Dashboards Power BI provides customizable dashboards that enable businesses to create tailored visualizations of their sales data. You can choose from a wide range of charts, graphs, and tables to create visualizations that are easy to understand and provide valuable insights into their sales performance. Integrations With Various Data Sources Power BI integrates with a wide range of data sources, including Excel, SQL Server, and Salesforce, among others. This integration enables businesses to collect their sales data from different sources and analyze it in one place, providing a comprehensive view of their sales performance. Data Visualization Power BI provides rich data visualization capabilities that enable businesses to create interactive visualizations of their sales data. These visualizations are easy to understand and provide valuable insights into sales performance, enabling businesses to identify trends and make data-driven decisions. Collaboration Power BI enables businesses to collaborate and share their sales dashboards with team members. This collaboration enables businesses to work together to analyze their sales data, identify trends, and make data-driven decisions. How to use Power BI Sales Dashboard to Gain Better Sales Insights? You should follow a structured approach towards achieving the best results from the Power BI Sales Dashboard. Here is how you can use it effectively: Step 1: Connect to Your Data Source The first step to using the Power BI sales dashboard to generate better sales insights is to connect to your data source. Power BI can connect to a wide range of data sources, including Excel spreadsheets, SQL Server databases, and cloud-based data sources such as Salesforce. To connect to a data source in Power BI, follow these steps: Step 2: Create a New Report The next step is to create a new report in Power BI. A report in Power BI is a collection of visualizations that you can use to analyze your data and gain insights. To create a new report, follow these steps:  Step 3: Build the Dashboard After creating your report, the next step is to build your sales dashboard. A dashboard in Power BI is a collection of visualizations and other elements that you can use to monitor your sales performance and gain insights. Follow these steps to build a dashboard in Power BI: Step 4: Add the KPIs to the Dashboard Key Performance Indicators (KPIs) are specific metrics that you can use to measure the performance of your sales team and business. Adding KPIs to your sales dashboard can help you quickly identify areas of strength and weakness in your sales performance. To add KPIs to your dashboard, follow these steps: Step 5: Add Filters and Interactivity Adding filters and interactivity to your sales dashboard can help you gain deeper insights into your sales performance. Filters allow you to focus on specific data points or periods, while interactivity allows you to drill down into specific data points to gain more detailed insights. You can add filters and interactivity to your sales dashboard as follows: Step 6: Publish and Share the Dashboard Once you have built your sales dashboard in Power BI, you can publish it to the Power BI service and share it with others in your organization. To publish and share your dashboard, follow these steps: Expert view: Following these steps can be a bit overwhelming for a business. It is suggested to opt for a BI consultant to accelerate the transformation journey.   Tips for Using Power BI Sales Dashboard Effectively When using the Power BI sales dashboard, it’s important to keep in mind some tips that can help you use it effectively: Keep the Dashboard Simple Avoid cluttering the dashboard with too many visualizations and KPIs. Instead, focus on the most critical KPIs that will help you gain insights into your sales performance. This will ensure that the dashboard is easy to understand and provides clear insights. Use Easy-To-Understand Visualizations Choose visualizations that are easy to read and understand, such as bar charts, line charts, and tables. Use colors and labels effectively to highlight important data points. Regularly Update the Dashboard Keep your sales dashboard up-to-date by regularly updating it with the latest

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3 Benefits of OEE in Manufacturing: Take Your Business to the Next Level

As a production company, maximizing your equipment’s productivity and tracking production in manufacturing is crucial to maintaining profitability and staying competitive in the fast-paced industry. One way to achieve this is by tracking your Overall Equipment Efficiency (OEE) and leveraging the insights gained to optimize your operations through data-driven decisions. In this blog post, we’ll explore what OEE is, why it matters, and how you can start tracking it today using automated systems and data analytics. What is OEE? OEE or Overall Equipment Efficiency tracking is a data-driven approach used to measure the effectiveness of your equipment in producing goods or services. It considers three key factors: availability, performance, and quality. These factors are then multiplied together to get an overall OEE score, which ranges from 0 to 100%. What Are the Factors Affecting Equipment Effectiveness? Availability Availability refers to the percentage of time your equipment is available for production. If your equipment is frequently down for maintenance or repairs, your availability score will be lower, leading to equipment downtime reduction. Performance Performance measures how well your equipment performs when it’s running. This includes factors such as speed, setup time, and changeover time, which are key factors for production efficiency. Quality Quality refers to the percentage of products or services that meet your quality standards, which are critical for maintaining customer satisfaction. If you produce a high percentage of defective products, your quality score will be lower. What is an Ideal OEE Score? Here are 4 OEE (Overall Equipment Effectiveness) benchmarks commonly used in manufacturing: Benefits of OEE in Manufacturing Tracking your OEE provides several benefits to your manufacturing unit. Some of them are: 1. Identifying Inefficiencies By tracking your OEE, you can identify areas of your production process that are causing bottlenecks or downtime, leading to improved production capacity. This also allows you to make data-driven decisions on how to optimize your operations and improve productivity. 2. Increased Profitability  Improving your OEE score directly correlates to increased productivity, which leads to higher profitability, a key advantage in a fast-paced business environment. By maximizing your equipment’s efficiency, you can produce more goods or services in less time, reducing costs and increasing revenue. 3. Improving Quality By measuring quality as a factor in your OEE score, you can identify areas where your products are not meeting your quality standards. This allows you to take corrective actions to improve product quality and customer satisfaction. How to Start Tracking OEE? To start tracking OEE, you can use automated systems such as sensors or software to gather data on your equipment’s availability, performance, and quality. This will allow you to calculate your OEE score using the following formula: OEE = Availability x Performance x Quality You can then use this score to identify areas of improvement in your production process and take action to optimize your operations through data analytics. OEE tracking is a simple yet powerful way to improve the efficiency and profitability of your production company. By production tracking, identifying inefficiencies, increasing manufacturing capacity, increasing productivity, improving quality, and proper industrial production capacity utilization you can stay competitive in the manufacturing industry and gain a competitive advantage in this fast-paced business environment. Power BI for Overall Equipment Efficiency Power BI is a powerful data analytics tool that can help manufacturing units track their Overall Equipment Efficiency (OEE) by providing real-time insights into their product management performance metrics. Here are some ways Power BI can help with OEE tracking: 1. Data Visualization Power BI can help production companies track OEE by providing real-time data visualization dashboards that can show the performance of each machine or equipment. This allows production managers to identify areas of inefficiency in their production process and take corrective actions quickly. 2. Integration Power BI can integrate with different data sources like Microsoft Excel or SQL Server to collect and analyze data from different production systems. This makes it easier for production managers to get a complete view of their production process and identify the root cause of any issues. 3. Customizable Reports Power BI allows production companies to create customized reports showing different KPIs (key performance indicators) related to OEE trackings, such as machine utilization, downtime, and production yield. This helps production managers to identify opportunities for improving production efficiency and profitability. 4. Machine Learning Power BI can be used to implement machine learning algorithms to predict potential issues with the equipment or machines. This allows production companies to take preventive measures to avoid downtime and improve overall production efficiency. 5. Mobile App Power BI can be accessed through a mobile app, which allows production managers to monitor the production process from anywhere and anytime. This provides real-time visibility into production operations and helps managers make informed decisions quickly. Best Practices to Improve OEE in Manufacturing These are some of the best practices that should be implemented to improve OEE: Rowing in a Digital Partner Successfully implementing OEE can be a challenge for some enterprises, hindering their ability to improve it. Assigning a digital or project partner can help overcome challenges and offer complete accountability for OEE implementation. With a consultant/partner in place, issues can be resolved, leading to successful implementation and sustainable changes. Real-Time Data Monitoring and Analytics Today, manufacturers should avoid using outdated manual data collection methods and managing paperwork or Excel files. Instead, they should adopt real-time monitoring to identify performance issues and analyze data for identifying trends and areas for improvement. This enables them to promptly address issues before they escalate into major problems. Implement Continuous Improvement Programs Continuously review and improve processes to identify and eliminate waste, reduce downtime, and improve overall equipment performance. Encourage employees to participate in improvement initiatives and provide them with the necessary training and resources to make changes. Implementing Root Cause Analysis Root Cause Analysis (RCA) is recommended as it is a methodical problem-solving process that identifies the fundamental cause of a problem. By addressing the root cause instead of symptoms, the recurrence of any equipment problem can

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DataToBiz Celebrates 5 Years of Empowering Enterprises Through Data-driven Insights

DataToBiz, a global entity empowering enterprises with data-driven solutions for 5 years and counting! Their team of experts has unleashed the power of data to fuel growth across industries and tech hubs. “Data transformation is the alchemy that turns data to gold, and we are data alchemists for 5 years now,” says Ankush Sharma, Co-founder, and CEO, DataToBiz. DataToBiz, a leading global data science, and digital transformation firm headquartered in Chandigarh, India is proud to announce its 5th anniversary in the industry. Over the past five years, DataToBiz has fueled businesses by unlocking real business value from their raw data, leveraging cutting-edge technologies into business systems, and providing data analytics expertise and innovative solutions. A go-to choice for enterprise success Since its establishment in 2018, DataToBiz has become a trusted partner for enterprises across various industries, offering solutions that help turn their data into actionable insights. For the past few years, they have stood as Clutch’s leader under the “Top artificial intelligence company in India”. The solid team of experts, data scientists, engineers, and functional consultants have helped clients process their raw data, streamline their day-to-day operations, automate their workflows, empower marketing tactics, and improve their customer engagement, among other digital transformation deliverables. Collaborations beyond borders Over the years, DataToBiz showcased continued growth at an accelerated pace in major tech regions of North America, Europe, the Middle East, APAC, and South Africa. With an exponential growth of 57% YOY for the last year, they now hold multiple partnerships in several arrays within the same regions. DataToBiz at its core has a solid team of experienced consultants across multiple domains along with cutting-edge technology experts, ensuring 360 degrees of business growth by providing both consultation and implementation on business intelligence, data warehousing, artificial intelligence and automation, data analytics, NLP, and offshore team augmentation, etc. Along with the business expansion over major tech hubs, DataToBiz stands as a trustworthy partner for its customers and clients over the period of 5 years. They ensure serving the existing clients with end-to-end solutions and successfully addressing the dire needs of digital transformation from their partners. Innovative offerings Apart from driving digital innovation through custom implementation, DataToBiz in-houses two live AI-based product lines including PrepAI, the most advanced AI-powered question-generation platform, and HirelakeAI, an AI-based intelligent hiring solution, along with other smart automated solutions under development. “The enthusiasm within us of completing 5 years is real, and we want to thank our clients, partners, and employees for their continuous trust and support” adds P.S Dhillon, Co-founder, and COO, DataToBiz. “We look forward to upcoming decades of driving innovation, delivering value, and making an impact with the power of data.” Originally Published on Digital Journal

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