Organizations have been gathering, analyzing, and saving data for a very long time but the real power of data is yet to be realized. Both the private and the public sector organizations have realized that the value of their data needs to be unleashed entirely at a never before faster pace for emerging victorious and having good growth.
Data education is necessary for an in-depth understanding of their data. As per Gartner, by the time we reach 2023, data education will turn as the essential driver of business value, exhibited by its official authorization in more than 80% of data as well as analytical strategies and programs involving change management.
Generation of business merits from data
Even though reducing risk remains crucial, the generation of data assets with value has turned out to be the key objective of the CDO (Chief Data Officer).
Organizations spearheading the advancements in technology have realized that data analytics should be the primary driver of business resolutions. Getting to understand their data is crucial in all aspects.
A Qlik Accenture case study revealed that 32% of business employees conveyed that they are capable of generating measurable values from data and some 27% conveyed their projects involving data and analytics generate perceptions that can be put into action.
Most organizations have no choice but to depend on the available specialists, analysts with experience, and senior staff members with the required skillsets for translation of insights and the intended meaning from the organization’s data.
This process eats up most of the time and may also contain errors due to human intervention or just the enormous volume of data that are processed by these limited specialist resources.
The main question that arises is the way to get a detailed understanding of their data. Employing an additional data-educated workforce is also not possible always that too in competitive job markets.
Importance of data education in business results
Data education Index of Qlik in 2018 identified that data-driven sectors had the benefit of an increase in their corporate performance that resulted in a greater enterprise value of 3-5 % relating to 500 US million dollars when adapted to the sectors incorporated in the study.
Business leaders do not need to peruse the research for acknowledging the impacts in-depth data perceptions will have on business results.
Combination of BI tools and Data education
Organizations have resorted to BI (Business Intelligence) tools for gaining in-depth perceptions regarding their data. Various business tools like Tableau, data visualization, etc aid the organizations to decide better based on information. This is demonstrated via graphs, charts, and dashboards.
Research by Qlik revealed that 67% of the workforce globally obtain their data via BI. But only a few can interpret what the data means from these BI. AI can aid to interpret these visuals via natural language generation.
Usage of NLG
Though BI tools have imparted data education, it still needs wider knowledge and expertise to use them efficiently. NLG (Natural language generation) can be incorporated into BI that provides more data education across an industry, thereby permitting all the workers to have a wider data understanding.
NLG interprets the data and generates it in a simple language that can be understood by everyone and can help them gain an in-depth insight. But this does not stop here. It allows analysis to be repeated so that the trends, abnormalities that were not identified previously can be probed further.
NLG is available at all levels. It doesn’t stop with answering queries while diving deep into the data but also guides you to the places to dig. NLG is the next level in utilizing the potential of AI for improved decision-making and driving exact outcomes in business.
With the increase in these BI tools, the need to incorporate the required skills for data interpretation and tools implementation to all the users of data along with the analysts is also on the rise.
Businesses need to identify successful and structured methods to allow and upgrade data education across the industries. The other option is to sit out of the competition without the ability to tackle the accurate worth of the data they are seated on.
