In today’s competitive landscape, despite the unprecedented crisis, organizations continue to make investments in their analytics journey with the hope that they deliver value. While these investments are essential for transformational initiatives, companies fight an uphill battle to harness value or measure the effectiveness of outcomes in terms of data-driven decisions they take. A report from Gartner tells us that “only 9% of organizations worldwide feel they’ve reached a transformational level of maturity in BI and analytics”.
To win this battle, organizations need to redefine their capacity to automate and enable frictionless experiences between user teams and data and create optimal decision flows in their business.
All of this depends upon enterprises developing 3 key capabilities:
1. Organizing data to be analytics-ready
Bringing data from disparate data sources into a centralized datastore is just the beginning. Some of the pertinent questions that need to be answered are: What is the quality of the data? Are they ready for downstream applications? Is data arriving on time for you to make the right decisions? Is the data stack simple enough to withstand future changes?
Key is to provide users with a quick and easy way to access data across all sources and such data needs to be organized, governed, cleansed, transformed, enriched, and kept ready for analysis. Automating this into a simple, real-time data pipeline for continually ingesting and replicating enterprise data when and where it’s needed allows for continuous analysis for data.
2. Deriving insights the smartest way possible!
With rapid proliferation of data sources within an organization, it is becoming increasingly difficult for data analysts to cover all data. How do you decide which data is relevant and which is not for your users? Data that your department needs may be inconsequential to others. Sometimes datasets that were deemed unnecessary add more context to the current problem at hand. Organizations have no choice but to make sure all data is covered for analysis. This in turn leads to data noise as well as requires an army of analysts to sift through the data to unearth insights.
Key is to explore data without constraints (through conversations), automate the discovery of insights, understand patterns from the data, learn from the historical data, and separate the signals from the noise. Running machine learning models and on-demand scaling of processing power helps 100% coverage of data at lesser costs making data analysts redundant.
3. Understanding the know-how of where these insights need to be applied
Automating the discovery of insights from large amounts of data addresses one side of the problem but how relevant is this to your current processes, department, or industry? An insight applied without understanding the domain or context is not actionable.
Key is to incorporate industry, function ready templates, accelerators to filter the relevant insights, auto triangulate root causes, club them together to narrate a cohesive story of why something happened rather than fighting a storm of alerts or dashboards, reports thus enabling optimal decision-making for your user teams.
Businesses should thrive on optimal decisions
Having the right data, just when you need them is key to delivering optimal business value. It creates optimal decision flows in teams and augments growth by bringing humans and data closer than ever. When companies have an increasing trend of healthy decisions taken with less friction, it creates tremendous value for their analytic investments and drives true impact.
About the Author
Founder and COO