Many businesses use data insights to support their decisions instead of driving their actions. After all, data is only valuable if you can translate it into actionable insights. Your organization might be sitting on the world’s largest data pile, but it is useless unless you have the means to translate it into insights that drives your business. Gaining successful insights means figuring out what you want from your data—finding its value. Most of the business problems can be solved if we know the business pulse, that is from what is happening in the insights derived through the data to what is the next course of action on the basis of those insights.
Can we turn the masses of these datasets into actionable insights which are capable of driving decisions and delivering operational efficiencies.. The information age today calls for the data to speak by itself and not just throw passive reports. The problem with large sets of data is two-fold – First, they need to be processed & analysed properly as per the user requirement otherwise they won’t give you the appropriate results and secondly, the key takeaways or insights need to be understood otherwise they are just some excel sheets and graphs which will only tell highs and lows.
The financial sector sits on a humungous amount of customer data. Every client segment in the financial services Industry: be it banks, Fintech cos, wealth & asset management cos – each has a huge amount of data pertaining to customers. Among the data types, transactional data is the central peace, and then demographics, social data and of course the relationship data are few others. Insights are of paramount importance to the Business leaders and to the BI teams, since these personas engage the customer & sales team with the products & sales strategies on a daily basis.
Insights derived through Natural Language programming are used in various tools & techniques to solve the problem of providing key statistical highlights of the data, extracting valuable insights which are key pointers for business to act upon and prescribing actions to be taken, given the current state of data. The instances of insights one can get from data are which product within a particular product category is driving sales for a region in the country, Some consumers are spending proportionately more amount in a particular time of the day in few regions of the country based on seasonality & a Bank X’s sales team in ABC territory drives most revenue in the country due to more sales in XYX product.
Today, the Insight tools are gaining a lot of relevance and users are now moving from conventional BI tools to these tools to make more meaningful and action oriented impact on the business. It is time to move to Insights, rather than play pure BI.
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