• Gesture Team

The data maturity curve

Between the proliferation of data science, analytics and data transformation, many businesses are wising up to the trends that stand to set them apart from their competitors.

Companies are beginning the journey toward becoming data driven and by the end of the decade, every industry will be driven by some form of automation and adoption of artificial intelligence. But how does a company go from being data savvy to data driven to an industry leader that fully harnesses AI?

Enter the data maturity curve!

Before you can run you walk and before you walk you crawl. The data maturity curve allows you to make smarter decisions about how to get to the next level in being able to leverage all the fun AI and ML tools you read about. Whether you’re at the beginning of your digital transformation journey or you’re already using models in deployment, this can serve as a handy guide to get a handle on where you are along this data maturity curve and where you want to be to reach your business goals.

What are the main components of data maturity?

Phase 1: Very little leverage of data and analytics. Perhaps you’re only using excel and google sheets to store your data and only gather insights for reports used in strategy meetings. Companies at this phase don’t have any automation in capturing, storing or analyzing data and often have to resort to manual efforts for even the most basic of data visualization and analysis. The company is aware of what they need to do to become data driven but there hasn’t been sufficient investment in making this a reality.

Phase 2: One off data projects and reports are being used but aren’t embedded into workflows yet. What does that look like? Maybe you have a few business units that are using dashboards regularly to track their progress and inform their decisions, but there isn’t a shared source of truth in the data. This causes confusion and hesitancy in making decisions and optimally using data but it signals the beginning of forming a data driven culture. Companies in this phase will need to focus on data modeling, database design, normalization and choosing reporting systems that can be used by more of their key teams and investing in hiring specialized data scientists and analysts. Baby steps!

Phase 3: Single source of truth exists in the form of a well-managed data warehouse or repository which is used by all business partners within the company for reporting, data science and analytics and key stakeholders are assigned to ensure this reporting is accurate and well maintained. Data engineering is well established here and the company follows data best practices, but it’s primarily leadership that has a handle on the data with minimal data governance. The formation of a data driven organization has been cemented at this stage although the leverage of that data is still foundational. Companies here can focus on improving their data quality and integrating applications that can be used across the organization as well optimizing their data warehouse and analytics.

Phase 4: Permission is granted to more trusted members and power users of the organization to access, explore and analyses the data and the company is using operational analytics to drive more and more business decisions. Here you would see the most impacted teams within a company regularly using their data and analytics and routinely running queries to get the data they need and make data driven decisions across multiple levels of the organization. At this phase, you are comfortable with how your data is being stored, managed and used to generate insights and analysis, but you still wouldn’t be seeing sophisticated deployments of data products or machine learning algorithms.

Phase 5: Here you would have a dedicated team of data scientists and engineers working to deploy data and machine learning tools that would be planned, created and deployed quickly and intentionally to solve specific business problems. At this phase you would have an established data team that evangelizes a strong data culture within your organization with data analysts or stakeholder partners working within impacted teams to further support this core team, as well as a well governed data environment. This team would be high functioning and would contribute to thought leadership within the organization and beyond. The focus here would be on integrating your internal data with external data sources, mining, statistical model building and beginning to use predictive analytics.

Phase 6: The holy grail. At this phase, data analysis is built into most of the company’s processes and is accessible by almost all impacted departments across the organization. You have a dedicated data team that works on core products as we mentioned in phase 5 but here you actually empower all your departments to have the ability to seamlessly integrate data and insights into new business policies and processes. Measurement, analysis, results and action are built into the company ethos and your company is leading by example in the data community. The company is forecasting and planning based on projected data and deployed models have immediate business impact and are driven by predictive and prescriptive data. The key here would be that virtually no decisions are being made within the company without compelling data based evidence for those decisions.

Moving up the data maturity curve essentially allows you to grow your business and make smarter decisions by minimizing costs on tools and expertise over time as you realize the power and opportunity creating a data driven company offers.

Catch you on the upswing.

Irene Bratsis

Gesture Data Science Team


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