Why We Need One

In today’s era, successful organizations leverage their data and enhance their products and services with that.

Let’s take an example of retail: Amazon, Google, eBay, etc are using data to find consumer behavior to recommend the right product to the consumer. Similarly, merchants also need to understand the ever changing market to understand its trends and upgrade their product or services accordingly.

It’s easy to say that you need to understand consumer behavior, but in order to do it, you would need various data points about the consumer, including age, demography, color, height, weight, their previous purchases, etc. A data-scientist can easily create the model IF you can provide them this kind of unrealistic data. A data scientist’s demand for data and the data supply are unmatching, which creates an unrealistic data element. Successfully companies can guess this information. For example, in order to infer the height and weight of a customer, you may look at the last purchases of the customers. In that, you might get an idea about the height and weight of the customer because of the size of their purchases.

This might be easy to conclude, but other problems may rise as well. What if 50% of your product doesn’t have a size? In that case, you may need some other clue.

This is why you need a data catalog. It allows you to look at hundreds of available data and try to find what data element can provide you that clue. For this, you would need to research the data from every possible source you have, understand it quickly, and collaborate with your colleagues, which would then lead to fruitful results.

To deliver any successful data product, it would need hundreds or thousands of assumptions, guesswork, algorithms. This is where a data catalog is handy; one can only imagine doing a product research without amazon or google.

A company collects and stores an abundance of their data, but it is inaccessible and unable to be analyzed. The plan to make data-driven business decisions is hindered at the very beginning. Here is some numbers about the current state of use of data:

Only 0.5% of all data is currently analyzed.

Only 14% of business stakeholders make thorough use of customer insights.

Organizations that leverage customer behavioral insights outperform peers by 85% in sales growth and more than 25% in gross margin.

  • Source: Datanami
  • Source: Forrester’s Q2 2016 Intelligence Enterprise Self-Assessment Scorecard
  • Source: McKinsey and Company

Top 3 reasons for Data Inaccessibility

  • Complex Data Stack
  • Dispersed Knowledge
  • Lack of Governance

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