How To Build A Data Management Strategy With A Data-First Mindset

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Rex Ahlstrom is the CTO and EVP of Growth & Innovation at Syniti, a leader in enterprise data management.

Data underpins business success. As a result, more organizations are making substantial investments in data management strategies. A survey by HFS Research and Syniti found that 65% of respondents had fully implemented data modernization. This entails the transfer of data from old, primarily relational databases to those in the cloud and those that can store unstructured data.

This is positive news, but data management strategy goes beyond data migration. To deliver increasingly experience-led outcomes, it’s crucial to understand the data lifecycle and put data first. Moving beyond mere migration requires building a data strategy designed for the long term, one developed for and guided by desired business outcomes. Core to this is a data-first approach. Organizations using this approach are already showing success; a study by ESG found that data-first leaders were 20 times more likely to beat competitors to market by multiple quarters (pg. 6).

How do you establish a lasting data strategy predicated on the concept of data-first? It comes down to people, technology and business outcomes.

Setting Up The Right Roles And Responsibilities

Data migration isn’t a purely technical topic. Data is intrinsically connected to business processes. It’s important to get the business experts, the “data owners,” involved from the start. This is what’s known as a data-first approach; it’s what companies need to transform data into a high-value business asset. It sets the stage for business transformation that yields a stronger competitive advantage, more efficient processes and higher profits.

Without the data owners’ participation, this project won’t work. They’re the experts in the processes underpinned by the data, whether it’s procurement, marketing, production or another department. They bring a functional view to the project. The migration is just a means to an end. If you don’t do it in the context of the business process, you’re just moving ones and zeros. There’s no value creation.

The other side of the coin is the technical people, those who work closely with the line of business owners to execute the migration. These are the IT people who understand the tools, the steps and what needs to happen next.

For a successful data management strategy, you need to bring both sides together to work collaboratively. It’s a team effort.

Selecting The Right Technology

You need to look at technology that’s based on persona, not just on function. There are a lot of solutions sold as data quality or master data management (MDM) tools, or governance, metadata management or catalog tools. But you need to be thinking about whether this is a tool for IT or whether it supports the personas across the business who are actual data contributors or stakeholders.

Are the tools accessible and useful for the persons who are involved in that pursuit, or are those people reduced to just sitting in meetings and giving approvals? The tools enable the types of things that people want the ability to do from a business persona perspective. When it comes to data management versus data mining or data science, the technology they want depends on their assigned roles and tasks.

Four Steps To Ensure Strategy Is Tied To Business Outcomes

No data management strategy will be successful in the long term if it’s not directly linked to business outcomes. As IT and business teams struggle to do more with less, there’ll be increased pressure to make the ROI case even before purchasing new tools. Historically, there’s been a missing link between tool implementation and recognition at the executive level of the tool’s importance.

Data management is a technical challenge for many enterprises, one that’s primarily internal. Poor governance and a lack of monitoring are the primary factors cited as the causes of faulty data. As a result, the opportunity resides in a more comprehensive grasp of data and a more potent means of driving change so that data matches up with corporate goals. Here are four steps you can take to get the most out of all your tools.

1. Support the data lifecycle. Adopt a workflow-centric perspective and concentrate on the strategic value of data. Data operationalization focuses on business objectives rather than technological capabilities by bringing IT and business together.

2. Determine the adoption of data quality and management tools. The market has eagerly and broadly acquired data management and quality tools. However, the benefits are still unclear. Alignment with data management standards is even less clear. Consequently, higher-value capture is about using the tools and ensuring they don’t just end up sitting on a shelf.

3. Promote change management. Drive effective change management and culture change to produce higher levels of good and usable data. To better comprehend the data lifecycle, pay attention to governance and monitoring. Understanding processes from beginning to end must be the focus.

4. Facilitate business outcomes. The support and facilitation of business outcomes must be your guiding light. Although managing data is primarily a technological issue for most businesses, this calls for doubling down on culture change. Your objectives for data management must be clear and measurable.

Managing Data Strategically

Companies are making more investments in data overall, but they’re still leaving money on the table. Data can’t be treated as an afterthought; it must be addressed early on, or organizations face cost overruns, unreliable analytics, project delays or even failures. Starting data work before or at the same time as the design phase of any digital transformation project is what will separate successful businesses from their counterparts. This will also be key for companies looking to drive value and innovate with generative AI.

High-quality data helps businesses experience exponentially better insights and outcomes. Now, it’s time to close the gap between what companies have invested in and how that investment is being used in terms of data management.

Organizations understand and appreciate data quality, but to produce business results, IT, LOB and a larger ecosystem must work together. Creating a lasting management data strategy requires putting data first. Use the four steps noted above to manage data well and use it to achieve your goals.


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