In this special guest feature, Soumendra Mohanty, Executive Vice President and Head of Analytics at LTI, provides five imperatives to establish a data-driven decision-making culture in your organization where there is a seamless exchange of ideas and insights. Soumendra is an acclaimed thought leader and subject matter expert in Analytics, IoT, AI Cognitive and Automation space. His expertise is in big data solutions, BI architectures, enterprise data warehouses, customer insight solutions and industry-specific advanced analytics solutions. With more than 20 years of industry experience, Soumendra has designed and implemented data analytics solutions for Fortune 500 clients across industry verticals
Enterprise data is varied, complex and often transcends departmental silos and line-of-business purviews. Data can also be dumb, unless it is leveraged as an enterprise asset. Its transformational value is only realized in a top-down, collaborative organizational ecosystem, combining data engineering and analysis with domain expertise. Analytics projects that are fruitful realign the organization’s culture to make sure everyone knows the value of the data and how to make the most of it.
Follow these five imperatives to establish a data-driven decision-making culture in your organization where there is a seamless exchange of ideas and insights.
1. Business outcomes first
First, identify the actionable insights you need to advance business outcomes. People tend to put data, insight and intelligence all in the same bucket. But data is the raw ingredient that needs to be processed to deliver insights – insights and intelligence are what the business needs. Also, be sure the business decision makers are in the project planning up-front so there is top-down program sponsorship.
2. Have a broad analytics strategy – but focus on quick wins
Many organizations get carried away with the analytics hype, investing in expensive data platforms and hiring data scientists that fail to deliver business outcomes. Your initial data analytics project strategy should focus on multiple, smaller projects executed with agility to deliver the actionable results and rapid value. This will infuse confidence in the project with the business and extended teams.
3. Sophisticated algorithms are mandatory but so is the art of storytelling
Many a times, outputs of algorithms are so cryptic that it becomes a humongous task to convey its meaning to the end consumer. The way the insight is presented is a question of appropriateness rather than power and sophistication of the algorithm. It’s about visualizing insights in a way that is relevant, making it easier to see the actions and how the data can be used in the business.
4. Analytics-led transformational outcomes require silo- and barrier-less organizations
Enterprises need to create a collaborative, cross-functional culture to consistently realize rapid value from data science. The project team should be comprised of business people who can frame the problems that need solving. This includes the data scientists and data analysts who can understand the data; data engineers and data wranglers who can put it all to work; domain experts to ensure the analytics outputs are validated and clearly presented; and a few storytellers who have a strong understanding of the business and present insights in a practical way that can be easily and reliably acted upon.
5. A robust data platform with a scientific approach to data science
To acquire, store and process massive amounts of data, you need robust data analytics platform, no doubt about it. Amazing capabilities can turn data into insight – spotting correlations and hidden relationships – and additional tools blend data and identify further relationships. These capabilities help the insight generation process. However, over-reliance and undermining the value of a scientific process to data science can induce errors, strengthen false assumptions and ignore key factors. Successful analytics projects not only leverage the capabilities of robust data analytics platforms but also pay detailed attention to the insight generation process to ensure that correlations are linked to causes.
Having loads of data and multiple data scientists are just parts of a successful analytics project. The five imperatives outlined in this article are necessary if you want to achieve the real transformational power of data science and analytics projects.
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