data analytics services Archives

Yashica Chopra
June 14, 2025
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Industry-Specific Analytics For Leaders – Key to Better Decision-making

Data analytics is essential to understand customers and markets and plan the business’s future steps to achieve its goals. Here, we’ll discuss the need for industry-specific analytics and how it can empower organizations to make better and profitable decisions. Data analytics is a keyword in today’s world. Every business wants to invest in analytics to gain a competitive edge. The market offers numerous data analytics and business intelligence tools for analyzing datasets and deriving insights. According to Fortune Business Insights, the global data analytics market was $64.99 billion in 2024, and is predicted to touch $82.23 billion in 2025. It is expected to grow at a CAGR (compound annual growth rate) of 25.5% to reach $402.70 billion by 2032. Artificial intelligence plays a vital role, as data analytics tools powered by AI and ML technologies can provide accurate and quick insights. However, with large amounts of data generated daily, how can organizations use this data for analytics? After all, statistics show that the global data creation will be 185 zettabytes by 2025. In such instances, the types of analytics you implement can determine your success. So, what kind of analytical insights should you generate and use for decision-making? Can general analytics provide the same results as industry-specific analytics? What is the difference between them? Let’s find out why industry-specific analytics are necessary for businesses in today’s scenario. Why is Generic Analytics Less Effective for Your Industry? Data analytics is the process of examining vast datasets to identify hidden patterns and trends and provide useful conclusions or interpretations. These are called insights and help in making data-driven decisions. Business intelligence, reporting tools, and advanced AI analytics come under data analytics. While the tools and technologies used are different, the central concept of data analysis remains the same. However, generic analytics are not as effective as analytics tailored for the business and industry. That’s because of the following reasons: Lack of Specifics Generic analytics are just that and offer one-size-fits-all insights that don’t go into specifics. They can be broadly applicable but miss the nuances of how things differ from one industry to another. Industry standards, business KPIs (key performance indicators), the organization’s mission and objectives, or even the target audiences are not considered in generic analytics. There is no specific indication that the insights will help your business handle a certain situation effectively. Misinterpretation or Inaccurate Data Without customized data analytics services, you have to rely on generic insights that may have misinterpreted the context or used a different dataset for the purpose. For example, a business manufactures and sells wooden kitchen appliances. To derive meaningful insights, it has to use data belonging to the kitchen appliances niche, especially items made of wood. Additionally, it should also consider the target markets. However, if it uses random data collected from the internet, the insights can be inaccurate and lead to wrong decisions. Risk of Biased Insights Since generic insights cannot offer nuance, they are not always actionable, as in, they are not always useful for decision-making. Moreover, there’s a higher risk of deriving biased insights since the data is not carefully collected or processed. For example, the insights might show that the sales haven’t been as expected, but fail to provide the real reason for this. Or, they could indicate a wrong reason, which ultimately results in extra expenses and losses for the organization. Lesser ROI When you hire a data analytics company, you want to get back the return on investment. The ROI is measured based on various metrics, like how actionable the insights are, whether the data-driven decisions helped achieve the business objectives, and so on. However, when the insights are generic, you cannot use all of them for decision-making. But you continue to spend money on the process. This reduces the ROI and indicates that your investment is not worth the money you spend on it. How Can Industry-Specific Insights Improve Your Forecasting Accuracy? Customized data analytics solutions for every business based on industry standards and requirements can increase forecasting accuracy and promote better decision-making at all levels in the enterprise. That’s why many data analytics companies offer tailored services that align with the mission, vision, and goals of each client. Here’s how industry-specific insights can help an organization be prepared for a better future: Targeted Insights Sector-wise data forecasting gives insights that target the industry, market, or customer base. This is done to get in-depth reports about how the various external factors influence the business and what can be done to make the best of the situation. When the insights derived are highly relevant, they help teams make empowered decisions to grow the business. For example, with targeted insights, you can understand why customers didn’t like a product or what can be done to increase sales. Strategic Decisions Since industry-specific analytics share insights about the patterns, trends, and correlations in historical data, they can be used to make informed decisions and build effective strategies to tackle various situations. For example, you can understand customer purchase patterns during different seasons to plan an effective marketing campaign and attract more sales. This increases the ROI for the amount spent on promotions and establishes the brand in the market. Market Expansion Every business aims to grow and expand into newer markets, increase its customer base, and achieve a higher share. For this, you should know which target audience to impress, how to convert them into customers, when to enter a new market, which products and services to promote, which marketing channels to use, and so on. The information to make these decisions can be provided by industry-specific insights. You can be ready for new opportunities and grow the business quickly. Customer Segmentation Customers are essential for any business to survive in competitive markets. However, retaining existing customers and attracting new ones requires a clear understanding of who they are, what they want, and how to convince them. For this, you should segment customers based on demographics, purchase preferences, likes, etc.,

Yashica Chopra
June 14, 2025
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Why Strategies Fail Without a Data Maturity Assessment Framework?

Data maturity determines the success of the data-driven decision-making model. Here, we’ll discuss data maturity assessment and how the framework is useful for a growing business in competitive markets. Data is the core of every business. You can no longer limit your data sources to internal systems and miss out on the valuable insights that external data can provide. However, collecting, storing, and using such large amounts of data can be a challenge for many organizations. After all, statistics show that the global data generated will reach 491 zettabytes by 2027. Whether you own a start-up, an emerging business, or belong to a multinational company, data management is a key process you cannot ignore. In today’s world, data-driven decisions give businesses a competitive edge. However, getting accurate insights requires high-quality data. This, in turn, requires standardized data engineering solutions, reliable data storage, and advanced analytical tools. Moreover, the entire process has to be streamlined and monitored to ensure consistent results. To summarize, the business requires a robust data maturity framework. If you have difficulties handling data or ask why your dashboards are not reflecting real-time or accurate metrics, this blog is for you. Let’s find out what the data maturity assessment framework is and how it is essential for your data strategies to be successful and deliver expected outcomes. What is the Data Maturity Assessment Framework? Simply put, data maturity is how well a business uses data to create value. It is about how integral data and insights are to the process of business decision-making. This is a primary part of initiating the digital transformation journey and adopting new technologies to boost your business. Data maturity assessment is the process of measuring your efforts in data management and usage. This assessment is used as a framework to evaluate the extent to which a business collects, stores, analyzes, and uses data to make data-driven decisions and whether or not the processes are aligned with one another and with the organization’s objectives. Data maturity assessment framework measures your data capabilities using different parameters, like data quality, data storage methods, data governance, data security measures, compliance standards, data literacy, data culture, the types of technologies used, etc. Before that, we’ll learn the reasons for data strategy failure due to the lack of a data maturity framework to guide the organization. Reasons Why Data Strategies Fail Without a Data Maturity Assessment Framework Creating data strategies is just one part of the process. The strategies can give results only when they are aligned with the business mission and objectives and are supported by the right technology. Lack of Understanding Do you have a clear picture of your business processes? Do you know the challenges your organization faces? Are the decision-makers aware of the concerns? The best way to get a detailed insight into your data management standards and processes is to fill out the data maturity assessment questionnaire. This helps evaluate the existing data and analytical systems in the business. No Communication The communication channels in an organization should go both ways. The top management and C-level executives should consider input from the middle-level managers and employees. They should keep employees updated about the changes being planned and how these will affect them. Open dialogue is essential to prevent misunderstandings and incorrect interpretations. Make clear communication a priority to build a data-driven culture in the business. Talent Gap New tools and technologies require new skills like data analysis, AI engineering, etc. If you are yet to begin the digital transformation journey, there’s a high possibility of a talent gap in the business. It implies that there’s a gap between the expertise of existing employees and what is required to strengthen the data-driven model. This gap can be filled by hiring new employees, augmented teams with external professionals, or partnering with a service provider who offers end-to-end, tailored solutions and long-term support. Lack of Data Literacy Data literacy is the ability to read, comprehend, process, and use data effectively in a business. A business that derives meaningful and actionable insights from data and makes decisions based on these in real time is said to have high data literacy. This includes employees’ and top management’s ability to work with data and technology for day-to-day activities. Employee training is the best way to increase data literacy. Outdated or Insufficient IT Infrastructure The IT infrastructure has to be upgraded regularly to prevent delays and mismatches of software. When a business doesn’t have the technology it requires, it loses opportunities to stride ahead in the markets. Legacy systems can be upgraded or replaced with cloud-based tools like Power BI to provide real-time insights and automated reports. However, you should choose the right technology. It should align with the business objectives. Resistance to Change It’s not uncommon for employees to resist change. Sometimes, even the top management is wary of new developments as they involve expensive upgrades. However, this can lead to stagnation, delays, and low performance. With many enterprises adopting new technologies, resisting change can increase competition and put the business in an unfavorable position. Talk to experts and reliable data engineering companies to understand how the right technology can give your business a fresh boost and a competitive edge. Low Data Quality Statistics show that businesses worldwide lose $15 million per year due to bad data quality. Poor data quality is when the data used by organizations is not cleaned. It has duplicates, missing details, and data in different formats. This can affect the accuracy of the insights. Data maturity assessment results indicate the extent of the loss. They also provide a clear picture of the current situation in the business. You can make the necessary changes to improve data quality by partnering with a service provider. No Regulatory Compliance Businesses should comply with data protection laws like GDPR, etc., to ensure confidential data is kept safe from unauthorized access. This is also necessary to avoid lawsuits and penalties. Lack of proper data strategies and management leads to