Digital transformation isn’t just about investing in modern technology tools. In an era where data volumes are ballooning, long-term transformation is only possible when companies curate, implement, and manage the right data-driven strategies across every aspect of their business and streamline the data management process.
Common Analytics Challenges
Almost every organization today understands the importance of investing in a data platform to enable insights into company performance, workforce productivity levels, security risks, application functionality, and more. Yet, when it comes to realizing true value, there are several stumbling blocks.
- The lack of a clearly defined data insights strategy causes organizations to achieve only temporary, short-term benefits instead of sustained, long-term ones.
- Ineffective data quality maintenance and data governance put the business at risk of evolving security threats and risks.
- Poor KPI measurement restricts data scientists from understanding how their analytics program is fairing and the steps they need to take to improve performance.
- The inability to quantify business benefits makes ROI justification of Business Intelligence solutions difficult.
- Siloed or partial analytics solutions result in not getting the best value out of the data or the platform.
- The velocity, variety, and volume of data make effective data management extremely tough.
Expert Insights: Driving Business Transformation With Analytics
If businesses want to drive effective transformation, they must embed data into the core of everything they do. We got in touch with two of our esteemed data connoisseurs, Debbie Zelten, Practice Director of Custom Applications and Data Analytics, and Shail Rathi, Practice Director of Business Intelligence. We asked them how organizations should go about crafting their data-driven strategies for business transformation. Here’s what they had to say:
Debbie: Organizations have different business strategies about who they want to be and how they want to compete against their competitors. Before they embark on their data journey, they first need to be clear about their goals. For instance, they need to understand if they want to be a low-cost provider of goods like Walmart or more of a customer loyalty organization like Nordstrom. This underlines the data insights strategy they need to build that maps to their business strategy. This also lays the foundation of what metrics they need to capture and monitor, not just across one area of the business but the enterprise as a whole. The KPIs/use cases/metrics need to be defined around the business strategy.
Shail: I agree. Analytics must happen across all aspects of the business. Just looking into sales analytics or simply unearthing insights into production efficiency won’t be sufficient. If businesses want transformative changes, they should not go for a point solution for one department. It might enable success or growth in one critical area of their business; unless they have this strategy going throughout the organization and everybody participates in it, they will not be able to leverage sustained benefits.
Therefore, in addition to having a data insights strategy, organizations must also have a long-term roadmap with parallel tracks for analytics across all departments. A robust data insights strategy, combined with a roadmap that runs maybe longer than a year, can enable organizations to have a vision that they can keep progressing. Having an enterprise-wide analytics strategy across all business functions is critical to competitiveness.
Debbie: Yes, absolutely. Along with a roadmap, businesses must also develop KPIs that seep across the organization. Instead of crafting very specific KPIs, say sales metrics or finance KPIs, they need to be created to measure performance across multiple aspects of the business.
Building a data-driven organization requires thinking outside the box about all the scenarios, even the more complex ones, and beyond traditional KPIs to get that competitive advantage. For example, if a business is a low-cost provider, it needs to have the right data to determine what its competitors are charging for each of the different types of SKUs. At the same time, it must analyze competitors’ margins versus its margins and keep harping on the enterprise-level strategy.
At the same time, data transformation must occur at the enterprise level with the necessary leadership buy-in. Businesses also need to invest in the right technology, the right analytics tools and techniques, as well as the right skillsets. They also need to plan their data management activities and set up the right data governance policies to safeguard the quality and privacy of data.
Shail: Looking at the current landscape, unless they have the basic foundational data platform and a unified way of pulling the data, they will not be able to advance to the next level of success. Instead of having the C-suite investigate business use cases where they can use machine learning and AI to the best at a later stage of the transformation journey, businesses must start early on.
A strong foundational data structure enables organizations to understand how best they can use data models and predictive analytics tools for racing ahead of their competition. So that should be part of every enterprise’s vision today, so they can successfully execute it in the shortest possible time.
Debbie: I’d like to cite an example here. A $1.5 billion industrial packaging organization did not have a data strategy from the executive level. With each business unit popping up its own solution for different areas, the IT department found itself dealing with 60+ different ERPs spread across the globe with siloed analytics being developed. With no strategic direction from the IT department, there was no clarity on what was needed from the business side.
For such organizations, IT needs to drive the standards of what different departments can and cannot do, how they can use data, how they can interface with the data, etc. This approach ensures that the data strategy is not a mess and that investment will not be wasted in redoing everything that’s been done. IT departments need to define their data strategy, including how it will be implemented and how the business can use it.
Shail: I’d also like to add that building such a strategy or curating such a plan is not always going to be easy. Change management is one of the most critical aspects such organizations must deal with. They must consider disrupting their culture, which is hindering them from being data-driven. We see many enterprises striving hard to come out of their manual culture, but such a transformative change takes time. It also demands an internal stakeholder with that vision to become a change agent.
Opting for Managed Analytics Services is a great way for organizations to overcome this resistance and reach their data goals more easily.
8 Steps to Building a Data Insights Strategy
Enabling actionable and strategic decision-making via a well-planned and well-executed data strategy can be pivotal to achieving your business transformation goals. But building and deploying a data-driven strategy is just the first step. Once the strategy is in place, it is important to begin developing a data platform and clearly defining your metrics, KPIs, and use cases to support and drive your strategy.
To summarize, here are 8 steps you must take while building your data insights strategy:
Step 1
Be clear about goals to ensure alignment between your data and business strategy and lay the foundation for what metrics you need to capture and monitor.
Step 2
Instead of going for siloed solutions for different departments, embrace analytics across the enterprise and have everyone participate in it to enable sustained benefits.
Step 3
Understand the importance of analytics early in the transformation journey and determine how it can be used to improve business performance.
Step 4
Build a long-term roadmap for implementing analytics across all departments and have a vision you can keep progressing on.
Step 5
Come up with KPIs that seep across the organization and measure performance across multiple aspects of the business.
Step 6
Invest in a modern data platform to ensure data quality, faster access, and improved security.
Step 7
Enable data transformation at the enterprise level with the necessary leadership buy-in.
Step 8
Up your change management game by having an internal stakeholder work as a change agent to overcome any resistance and more easily reach your data goals.
Business Intelligence and Analytics Consulting With Synoptek
With BI trends constantly evolving, leaders need to really support this change and empower their teams to embrace the new way of doing things.
Are you looking to build the right data-driven strategies and drive comprehensive business transformation? Here’s how Synoptek’s Managed Analytics as a Service and Business Intelligence and Analytics Consulting Services can help!
About the Author
Shail Malpani Rathi is Practice Director, Data Insights at Synoptek. With over 22 years of experience focused on adopting advanced BI capabilities, she lays out an enterprise-wide BI Strategy, outlining a roadmap, and laying out architecture for a phased data warehouse implementation across on-premises, cloud, and hybrid environments. Her core expertise is leading customers for enablement of BI platform to meet their strategic goals and discover data insights. She has extensive experience working on data warehousing for healthcare, manufacturing, retail, nonprofit, education, sales, and marketing domains.