Struggling Analytics

In an increasing digitized economy – and digital world - analytics have a critical strategic role in how organizations pursue gaining the necessary competitive edge to be at the forefront of digital disruption.

Organizations are investing heavily in analytical solutions to improve their corporate decision processes, identify new business improvement opportunities, increase accountability, raise productivity, make better predictions, monitor performance, and specially in these turbulent times – to make decisions under uncertainty.

The awareness of the strategical importance of analytical solutions for an organization exists but most of the time the results will not follow and despite large investments in analytical solutions, big data, AI, Neural Networks, Machine Learning, Deep Learning, etc., most organizations still fall short of retrieving the meaning full insights and successfully integrate analytics into their decision-making processes.

Why?

Its’ paradoxical, we see organizations struggling to collect as much data as possible, we see infrastructure, storage, processing, and analysis investments increasing and at the same time we witness the quality of analysis and insights decreasing.

Some the causes for this are not related to analytics by itself, system, or infrastructure problems, but to what feeds them – Data.

When we look at some of the problems that are most frequently faced with business data analysis, and we see solutions that are unable to provide new, timely or even accurate insights, the causes usually are easily on data:

• Data that is not enough to generate new insights.

• Data sources that are no integrated.

• Data that is not available on time.

• Data that has defects, errors, is missing or incomplete.

• Data that is not adequate to a business case.

• Data belonging to rogue data sets.

• Data that can’t be traced to source.

• Data generated by undocumented transformations.

• Etc.

Data is the challenge

Data is the biggest challenge when employing new technologies and the impact of data-driven decisions is larger than ever before.

Data governance and data quality play critical in these processes, and prior to any investment on analytics, data governance and quality strategies must be in place to support and assure the return on this investment.

Owning and trusting data are two completely different things, the amounts of data available are increasingly bigger however that data is not suitable or trustable enough to make business-critical decisions with. This lack of trust in the data has a twofold impact, it either inhibits decision making, or even worse, it induces decisions based on false assumptions.

This can only be solved by developing strategies to address existing and ongoing issues and implementing effective methodologies to execute on that strategy.

A data governance program and structure covering the people, processes and technologies needed to manage and protect the organization’s data assets to ensure understandable, correct, complete, reliable, secure, and discoverable across the organization.

A data quality framework encompassing data quality discovery, remediation, and monitoring can help organizations approach the data quality issues that hinder the success of analytics initiatives.

This approach allows data quality processes within the organization to be performed in a more agile and scalable way, reducing the siloed approach and manual interventions. Resulting in an overall improvement of the value returned from analytics.

Considering that data is one of the organizations most critical assets, making data governance and data quality the basis of all analytics initiatives, leverages the value of data and unlocks new insights critical to create the necessary edge on an increasingly competitive business ecosystem.