A recent study, produced by Wakefield Research for Alation, details the state of culture and the data analytics agenda for organizations.
The most important finding is that building a data culture has a direct impact on a company’s ability to get or stay ahead of the competition — yet receiving the investment that data leaders say is necessary to do so isn’t guaranteed. This research shows that having a strong data culture is linked to achieving revenue goals; those with a top-tier data culture were more likely to have exceeded their revenue goals in the past 12 months.
Not Many Possess Top-Tier Data Cultures
The study produced some unexpected results. Most companies have a lot of work still to do to build a data culture. Just 15% qualify as a top-tier data culture using the study’s Data Culture Index (DCI), which ranks a company’s data culture across three disciplines: Data Search & Discovery, Data Literacy and Data Governance.
Part of the problem is that 71% of data leaders are less than very confident that their company’s leadership sees a link between investing in data and analytics and staying ahead of the competition. This points to a C-level strategy gap, in which company leaders pay lip service to the benefits of and the need for increased investment in data and analytics, but don’t prioritize analytics investment or recognize their vulnerability to disruption.
Another surprising result had to do with data maturity. In measuring the maturity of data and analytics, only 10% of organizations attested to having enterprise-wide data initiatives (a similar number reported having data governance initiatives).
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Business Reasons for Investing in Data & Analytics
What are the business reasons for pursuing data and analytics initiatives? The numbers were split relatively evenly between key business agendas:
- Business Efficiency: 40%
- Customer Experience: 31%
- Digital Transformation: 29%
Which departments pursue data and analytics the most? The survey found that finance is the top driver of data and analytics, followed by sales, operations and then marketing.
However, the data shows that for boards, the biggest agenda item is revenue growth. That’s true for Jim Russell, CIO and VP for digital strategy and planning, Manhattanville College. “I would say improving CX and institutional efficiency are part of the why for data informed transformation.” To deliver this, businesses need a coherent data and analytics strategy.
Data Strategy Must Begin With Business Need
Data leaders indicate that investment depends on where an organization sits in its data maturity cycle. Often, improving data maturity starts with end-user training. Key training topics include data literacy, communication skills, data quality and implementing tools.
IT staff skills are also vital, analyst Daniel Kirsch suggests: "Data leaders need to build their data and analytics strategy. It is critical that it starts with a business problem. You can't build a data strategy without a concrete business need.”
Top Data Challenges: Processes, Inventory, Literacy
Businesses know they need to use data wisely and strategically. So what stands in their way? Wakefield Research found that the following issues stand out for organizations that they surveyed:
- Creating Data Processes: 44%
- Creating an Inventory of Existing Data: 43%
- Finding Existing Data Quality Issues: 38%
- Building Master Data Management: 37%
- Creating Data Literacy: 37%
- Aligning on the Business Value of a Data Culture: 35%
Such statistics reflect larger challenges connected to data governance, quality and silos. Data governance is part of the process of addressing data quality. It remains important to establish a golden data source and integrate siloed data at both operational and analytical levels.
“Data silos and data trapped within business units is a huge problem,” Kirsch points out. “Organizations need senior leadership or a chief data officer with the power to mandate data sharing.” These data leaders need to put in place data culture, data controls, data transparency and tools that support collaboration.
It’s also important that leaders and stewards share an unflinching look at the results or quality of data. And data leaders need to enable agility; according to Jones, leaders should work to decrease the “latency between transactions and the ability for data to inform the decision.”
First Steps in Implementing a Data Culture and Data Literacy
Data leaders suggest that data culture is about belief systems, and data literacy requires an understanding that data has inconsistency.
In the survey, both perspectives were clear, as 44% said creating data processes was the most important step to establishing a data culture, followed closely by inventorying existing data and fixing data quality issues. Building an inventory (and process) requires building a strong coalition and discovering the data landscape. Aligning on language is important, and business and data teams should establish a vocabulary (terms, data dictionary and report inventories).
With these pieces in place, leaders can democratize data access — but with robust training. Kirsch says, "I see many organizations adopt data democratization strategies." However, without data literacy teams, organizations don't know what to do with data. How can you spot a blip vs. an emerging trend? Correlation vs. causality is critical. To remedy this, organizations should:
- Build a data framework and strategy
- Show leadership how it was foundational for their transformation portfolio
- Get buy-in to plan and cost
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Top Drivers of Data and Data Culture
Given previous answers, it was not surprising that 68% said a technical leader (with CIOs garnering the most answers) were the drivers of the data agenda. It is important whoever the leader is that they have understanding the value and necessity of data. As well, they need to be able to enlist senior leadership in paving the way forward.
Agreeing, Kirsch says that “adopting a transformational data strategy really needs top-down support. Everyone from the CEO to the CIO and CTO needs to support the strategy. Without this approach, business units will protect their data and the organization is destined to fail. To gain wide adoption across companies, data leaders need to get executive buy-in.”
Russell says, “Data strategy (is) tops down and bottoms up. It is like burning candles at both ends.“
I find that building a data strategy needs to have the support of the C-Suite and the frontline. Clearly organizations that are trying to become more agile need to push decision-making down in the organization and move from using gut feel to data. This is a big step but can be the difference between winning and losing against the competition.