Data Driven Decision- Sounds Great In Theory, But What About In Practice?

Data Driven decision Making

Data-driven culture is taking everyone by storm. The culture is successfully adopted by several organizations as it has succeeded in generating lots of reports, accurate ones. A data-driven culture is said when an organization’s progress is measured using data rather than intuition (gut feel) or past examples (personal experience). Now the term data-driven means the decision-making process which involves collecting data, extracting patterns and facts from that data, and utilizing those facts to make inferences that influence decision-making.

As a result, each and every industry aims to be data-driven. Today you will find no company or organization claiming that lets not use the data our intuition is enough to succeed. A data-driven culture is said where transparency and accountability are nurtured around data, all you require is the ability to develop strategic insights into what is influencing your key performance indicators (KPIs).

Now looking at the current scenario, when you try to foster a data-driven culture, teams are more likely to consider data as it will help in order to develop better strategies while dealing with data.

Data Driven decision Making

Down below I would like to mention certain challenges to solve to become data-driven:

Privacy is important

Considering the world of data-driven organizations, it becomes pretty transparent with the use of big data and leads to trust among companies, customers, and competition, driving conversion rates and sales. The risk of corrupted privacy, monopolization, and market manipulation has also been rising with Big Data. As a result, General Data Protection Regulation (GDPR) Act was required to prevent the occurrence of illegal activities.

Data remains in Silos

Being a fixed data repository, data silo with one department control is like grain in a farm silo isolated from other elements. Organizations believe a lot in a centralized data system but lack the right strategy in place to achieve this objective. As a result, data remains in silos and continues to be inaccessible to people who could make use of it. This factor proves to be a significant obstacle in an organization’s data-driven journey.

Data Integrity

Have you wondered what’s called data combined with integrity? A data model or a data type decides data values. Data characteristics, such as business relations, dates, lineage, definitions, need to be relevant for data to be complete. A database with imposed integrity should be designed and checked through error checking and validation routines. For instance, numeric cells or columns should not accept alphabetic data to maintain data integrity.

Having the right skills

According to Gartner, organizations try to meet their data skills needs according to their requirements. Data science includes a delicate balance between art and science and is more than just number crunching. Data savvy people who have the required skills are not easy to find.

Identifying the right technology

A plethora of data solutions are available these days. Amazon web series and several other companies are struggling to find the right one. Finding and implementing the best fit are the top challenges organizations face when shifting from a traditional model to a data-driven one.

What next?

  • Selection of relevant data
  • Improved decision making in real time
  • Implementation of data-based models

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