Adrian
3 min readOct 30, 2020

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A strategy, independently on whether applied to organizations, chess, and other situations, allows identifying the moves having the most promising results from a range of possible moves that can change as one progresses into the game. Typically, the moves compete for same or similar resources, each move having at the respective time a potential value expressed in quantitative and/or qualitative terms, while the values are dependent on the information available about one’s and partners’ positions into the game. Therefore, a strategy is dependent on the decision-making processes in place, the information available about own business, respective the concurrence, as well about the game.

Big data is not about a technology but an umbrella term for multiple technologies that support in handling data with high volume, veracity, velocity or variety. The technologies attempt helping organizations in harnessing what is known as Big data (data having the before mentioned characteristics), for example by allowing answering to business questions, gaining insight into the business or market, improving decision-making. Through this Big data helps delivering value to businesses, at least in theory.

Big-data technologies can harness all data of an organization though this doesn’t imply that all data can provide value, especially when considered in respect to the investments made. Data bring value when they have the potential of uncovering hidden trends or (special) patterns of behavior, when they can be associated in new meaningful ways. Data that don’t reflect such characteristics are less susceptible of bringing value for an organization no matter how much one tries to process the respective data. However, looking at the data through multiple techniques can help organization get a better understanding of the data, though here is more about the processes of attempting understanding the data than the potential associated directly with the data.

Through active effort in understanding the data one becomes aware of the impact the quality of data have on business decisions, on how the business and processes are reflected in its data, how data can be used to control processes and focus on what matters. These are aspects that can be corroborated with the use of simple BI capabilities and don’t necessarily require more complex capabilities or tools. Therefore allowing employees the time to analyze and play with the data, can in theory have a considerable impact on how data are harnessed within an organization.

If an organization’s decision-making processes is dependent on actual data and insight (e.g. stock market) then the organization is more likely to profit from it. In opposition, organizations whose decision-making processes hand handle hours, days or months of latency in their data, then more likely the technologies will bring little value. Probably can be found similar examples for veracity, variety or similar characteristics consider under Big data.

The Big data technologies can make a difference especially when the extreme aspects of their characteristics can be harnessed. One talks about potential use which is different than the actual use. The use of technologies doesn’t equate with results, as knowledge about the tools and the business is mandatory to harness the respective tools. For example, insight doesn’t necessarily imply improved decision-making because it relies on people’s understanding about the business, about the numbers and models used.

That’s maybe one of the reasons why organization fail in deriving value from Big data. It’s great that companies invest in their Big data, Analytics/BI infrastructures, though without working actively in integrating the new insights/knowledge and upgrading people’s skillset, the effects will be under expectations. Investing in employees’ skillset is maybe one of the important decisions an organization can make as part of its strategy.

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Adrian

IT professional/blogger with more than 19 years experience in IT - Software Engineering, BI & Analytics, Data, Project, Quality, Database & Knowledge Management