Data lake

Empirical Decision Making for Business Excellence

Sep 25, 2023

In Sales, Marketing and Business Development – as well as business strategy in general – taking decisions based on data (hard facts as it has been referred to in an earlier era) is not something new. In a HBR story, Kristina McElheran and Erik Brynjolfsson opine that at their most fundamental level, all organizations can be thought of as “information processors” that rely on the technologies of hierarchy, specialization, and human perception to collect, disseminate, and act on insights.

We create strategies and take decisions, especially in marketing, based on certain numbers, trends and assumptions based on those. There have been two sweeping changes in the last decade that have fundamentally altered the way we use data to make decisions. (1) The opportunities to collect and leverage data have changed dramatically with the advent of digital technologies (2) The very characteristics of data have changed even more dramatically – velocity, volume, variety, veracity – again thanks to digital technologies.

How do these two shifts affect decision-making? Statistics and technology are being combined to make sense of the huge amount of data at our disposal today to access the data, pinpoint observations, craft insights around them, and create actionable steps to enhance decision-making. Products and services are being shaped around our understanding of the data – not just in the way we target and reach the customers, but also the way we market our services. We are not talking about change here. We are talking about a paradigm shift.

The way to be smarter in this new journey is to go beyond the excitement of how the data is positively changing business. As the value of your data increases, it needs to be managed to ensure it is consistent, reliable and useful. When you are choosing a technology partner to help you effectively navigate the challenges of big data, and make data work for you – keep the following in mind:

  • You require data from all the internal and external sources, legacy and current, structured and unstructured – across its different types and forms; standardize it to make analytics-ready; make it easily and dynamically accessible for different users within your enterprise (from data analysts to marketing managers to sales staff on the field). This means moving away from traditional methods of extracting, loading and processing of data to more agile and scalable methods (NoSQL/NoETL) and a cloud-based data management solution without losing the data integrity.
  • You have to put context to the data you have managed to capture and profile to draw the right insight out of it. This requires capturing customer interactions with your brand event by event. No wonder then the marriage of artificial intelligence and data analysis is one of hottest data trends.
  • You will want to adjust your marketing or product or business strategies in real-time to take advantage of perceivable trends. This requires the entire value chain of data engineering and analytics to be agile, flexible, technology platform-agnostic, integrated with your enterprise technology eco-system, and responsive.
  • Staying ahead of the curve requires using predictive analytics to understand patterns in data and making business decisions based on pattern analysis. By intelligently leveraging artificial intelligence and machine learning, predictive analytics can become more reliable and robust.
  • You can understand the finer details of data by using Visual Analytics, which will make your decision-making faster.

Your search for the best data engineering and advanced analytics expert – who can enable you to understand Data, manage it, make it easy to work with it, and lead through it – will end at TIBIL Solutions.

Our cognitive, cloud-ready Data Lake solutions help you translate data into competitive edge. We enable organizations make intelligent decisions in real-time by integrating various data sources to create a data lake and developing the analytics layer leveraging ML/AI algorithms.

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