BlogsDemystifying the Science Behind Data and Analytics for Decision Makers

October 8, 2021by rubiscape

Almost every organization, government or otherwise, is going through a digital transformation journey. Many organizations are at different points in their maturity models.

Digital Transformation enables optimal resource utilization as well as increased customer satisfaction. For instance, sectors like automobiles have found out avenues to monetize the data on driving patterns by selling them to insurance companies. Essentially, data is opening up new revenue generation avenues for companies. Gut instinct-based decision-making is a thing of the past now, and every major strategic decision is data-driven. In such a scenario, data becomes a priceless organizational asset. 90 percent of the data available worldwide has been generated in the last two years. If the analysts are to be believed, then the revenue of any company can be increased by 66 percent by following best data quality and analysis practices.

But before embarking on the journey of being data-driven, one has to understand the key building blocks of a strong foundation.

  • Data – Data has already been categorized as an organizational asset used for making a strategic decision. As per Gartner, by 2022, for 90% of the organizations, data will guide business decisions. Already 69 percent of the organizations are using data from newer sources to understand their market. 64% of organizations have started using predictive and other forms of advanced analytics in their business. 67% are exploring new types of analytics to consume the data they have with them.
  • Analytics – The most common types of analytics include descriptive, prescriptive, and predictive analytics. Descriptive includes day-to-day reporting or operational reporting. Prescriptive analytics helps in coming up with recommendations. Predictive, as the name suggests, helps in predicting the future so that the organizations can reap the maximum benefits out of it.
  • Technology – Storage, collection, processing of this huge amount of data is being made possible due to the huge strides we have made in technology. There are tools like Python, Spark, Power BI, which enable us to collate, process, store, and visualize data.
  • People – This is one crucial aspect, as people resources and skill form the basis of any significant shift. Becoming data ready requires Data Scientists, Domain experts, and Technology experts. One has to be sure that these relevant skill sets are on board. There is a certain amount of cultural change which has to be brought in and is the result of getting the buy-in of the folks who will be a part of the job day in and day out. Organizations need to understand that this is a team effort, and not one single individual can change the organizational leanings overnight.
  • Processes – The whole value chain has to become data-centric. Elaborate and a foolproof method of collecting and processing data has to be set up. Data quality is of utmost importance, and the role of CDO has to be created who can ensure that there is a certain methodology in place to ensure sanity in the data process.
  • Strategy and Vision – What does the company want to achieve in the long run? The strategy and vision of an organization could vary across a broad spectrum of activities. It might want to reduce costs and become leaner. Whereas on the other hand, it might want to reach out to newer markets or develop newer products. Recently companies have also started exploring newer avenues of revenue generation. Depending on the needs, the data attributes being captured, and the metrics being measured change.
  • Operating Model – To succeed in the quest of becoming data-driven, one must understand that a robust target operating model should be set up. From the data office who will be interacting with whom and collecting what data and who will be responsible for the data quality – everything becomes extremely important for the smooth steady-state running of the operations

A strong data-driven culture, availability of the right data for analysis across the value chain, easy-to-use tools that can allow quick slicing and dicing of data, and more awareness around data-driven decision-making are some of the key factors for the success of data-driven enterprises.

Where are you on your data journey?

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