31st Jul, 2019
Denave, Team, Designation
Digital business landscape is not only reshaping every sector but is also enabling endless possibilities which are advancing the reach of business further.
What comes along is the massive data complexity which needs be harnessed to interpret it into meaningful insights to eliminate the guess work in order to diagnose the root cause of problem and support effective decision making. Hence, providing cutting edge competitiveness in the ever-changing business environment.
Data is flowing through various digital channels, social media platforms and mobile phones – enabling access to that vital information about the consumer behaviour which always alluded the decision makers or was wished by them.
Every business has established a formal set up within its structure to drive data-driven decision making. But this is only one part.
What is becoming more important is:
- Agility of decision making
- How rapidly the decision needs to be taken
- At what level of customer interaction is the decision taken.
Analytical toolsattempt to makes this possible through various dashboards engineered by packaging the information through structure and visualization as per the defined business metrics. However, this is also more of a one-sided approach or can be termed as more of push strategy.
What is equally important is the pull strategy which would entail building expertise and knowledge about emerging new data trends and patterns, investigating data correctness and building on existing reports.
The journey is towards the use of data to enhance and improve effectiveness of decisions taken by every stakeholder with right knowledge of data.
Ever increasing use of these platforms have become the sources of deep insights into consumer behaviour, changing tastes and preference, emerging trends and patterns which become the heart of product/ service proposition for companies and allows them to be ahead in the game.
New age solutions are focusing on accumulating data and insights from multiple interactions versus the traditional methods which were designed to perform in a certain way. Capabilities are being enhanced to interpret full spectrum of unstructured data. Machine learning has made it possible to structure the unstructured data formats into more decipherable formats which earlier would be a hurdle to scalability.
Decision making has moved from analysis of past data to current date, use interactive data and allow real time decision-making. All this means higher ability to put out tons of information which needs to be supported through automation of processes, easy access to information in the form of dashboards and further ability for users to customize the reports.
Business managers would be interested in solutions which cuts their dependency on data search, man hours put in to carry out the analysis. New analytics are offering solution which are cutting out dependency on IT teams, making data preparation smarter and shorter and unlocking the information from raw data and converting them in to intelligent insights.
Keeping pull strategy in mind solutions are now being designed to hunt relevant data source and map it against relevant content to impact the speed of analysis. Information can be visualized faster in desired impactful way to be used for communicating with the required audience, thus enabling faster collaboration and enabling faster decision making. This approach is helping build uniform approach across the organizations in a manner that how data is handled, studied, interpreted and communicated.
Tools can continue to be relevant for consumers only if they can be accessed easily and are user friendly while addressing the need for deep insights. Complex designs are hindrances which restricts the learning process, generates negative sentiment and restricts scalability and adoption.
What do you think about the evolution of BI and Analytics that the industry can expect? It will be good to see different perspectives from thought leaders.
This article was originally published here.