Data Analytics: Expectations vs. Reality

The difference between expectation and reality blends out easily with advanced virtual technology. It is essential to understand the advantages & limitations of all the technologies we adopt and are planning to adopt for our businesses. Technology can make things simpler, but when utilized differently can make things more complex.

  1. Lack of Data Points Collection:


All data will be well maintained for conducting proper data analysis. Data Analysis for a company is about hiring a data expert who can now use tools and software to respond to data-related queries and setting up processes to make data more efficient. He will be responsible for reading between all the critical areas, analyzing and interpreting trends from the data, and reporting those trends to add business some value. The business is ready to implement insights and solutions received as a conclusion from the entire process. There is a huge expectation from an analyst to generate actionable business insights.


Most entities’ most common mistake is to maintain the data wisely. There is no great importance given to information when it is generated. If you don’t collect data, the analyst has no suitable base to conduct an accurate analysis. No expert can create data for you; they can only read and examine it. You might need help to draw reliable results even if you pay the highest. To perform any analytics on data, there has to be data that is appropriately collected & stored in a usable manner.

  1. It’s all ML, AI & Algorithms:


AI, MI & algorithms are now trendy buzzwords in the data world. The only relevant technology in the coming years is going to be this! No wonder businesses are transforming with these technologies by their side & significant companies are investing in such future technology. We see ML, AI & algorithms replacing many things around us. The prominent usage of AI software in everyday life includes

  • Voice assistants,
  • Image recognition for face unlock, and
  • ML-based financial fraud detection.

Their role in business entities will be even more impacting & transforming.


Newer technologies are more innovative than all the older ones and are an excellent replacement for many human-based tasks. Yet they have their limitations. Data Analysis is beyond ML, AI & algorithms. For example, if there are “x” numbers of a wheat flour company’s sales and the sales decline after May. There has to be a possible reason for which the sales decreased. The reason was that consumers assumed that it had some plastic thing in it, which is harmful. Any ML or AI can not recognize such human tendencies. Hence it becomes crucial to predict consumer & human behavior. Data analysis is not only about numbers but also about human behavior. It provides a wholesome insight rather than just results based on numbers & diagrams.

  1. You Will Impact Business With Key Insights:


Data will entirely transform my business model and all associated activities. There is no specific need to take the extra burden for the business decisions as the data will now speak for itself. Moreover, data-driven insights are accurate for all business models & I can rely on such data as it is logical and valid. All the insights generated through the complete research & detailed analysis can be implemented as it is for the best results.


Data will show the same result to everyone for a specific value. But for the exact data value, all the businesses are unique & run on different capacities.

For eg: If data says for every 500kgs of production, you need to procure 750kgs of raw material. And there are two businesses, one having 800kgs of storage capacity & other having 400kgs of storage capacity. Should the insights be the same for all? All your key insights must be business-driven & data-driven & not just one. Applying key data insights also has to be backed by realities and not just assumptions & output of analysis itself.

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