Data Before Tech! Making the Case for Better Data

Business Disruptions Are a Wake-up Call to Data Leaders

Business interruptions, disruptions, outages, meltdowns, fiascos — call them what you will. The costs — ranging from lost revenue to reputational damage — can be severe. External events outside the control of the organization may accelerate or exacerbate issues, but they are rarely the root cause. But not all business disruptions are surprising, and some can be mitigated or averted altogether. Disruptions often occur due to years of procrastination in mitigating known risks or in automating manual processes.

Businesses look to IT infrastructure to improve business performance, so it should come as no surprise that IT is seen as a culprit when there’s a massive disturbance. But is focusing on tech and infrastructure enough? No. Organizations need to take a data-centric approach to IT modernization, investing in data first and structuring IT applications around it. Simply put, data is the most important asset, not the tech.

Why Don’t We Trust Our Data?

Time and time again, I work with clients who struggle to realize the value of their IT investments because of a lack of trust in their data. Our data advisors consistently hear that data quality issues are a pervasive problem for B2B organizations. A particular pain point involves the accuracy and consistency of data across platforms. 

Why is this the case? The trend of a technology-first approach to IT modernization is a key contributor, creating Frankenstein-like monsters of applications that are not able to talk to each other. When teams are not able to easily access critical data on customers, suppliers, and partners, the outcome is manual workarounds and data silos. Before long, senior leadership begins to question the value associated with the IT investments that were supposed to simplify and standardize the consumption of data. 

If your teams don’t trust their data, or even worse — if they do and it’s wrong — the business pays the price. Organizations can avoid going down this path by adopting a data-centric approach to IT modernization that will ultimately save time, money, and resources.

How to Be a “Data-Centric” Business

Being data-centric means having a business culture that values data over the adoption of individual applications and services. It means that an organization’s culture encourages team members to be data-driven and to leverage tools, analytics, and metrics to support their decisions. Data is valued as an enterprise resource; silos are discouraged, and data governance is a team sport. Data on customers and suppliers is prioritized, mastered, and governed; and the organization has commonly accepted definitions and attributes utilized across all platforms, enabling them to talk to each other.

What Is Good Data Quality?

Although there are dimensions of data quality that are important, such as accuracy, completeness, timeliness, and consistency,  the ultimate measure of data quality is whether the data meets the needs of the business. Data-centric organizations map their use cases back to the data needed to support them and put governance in place to benchmark and track data quality over time. Some use case questions to consider:

• Does your data support your go-to-market strategy?

• Are there commonly agreed-upon definitions of your most critical data (customers, suppliers, and partners) that enable applications and departments (and people) to communicate with each other?

• Do you enable enterprise visibility for critical functional areas like sales, analytics, finance, risk, compliance, and marketing?

• Is your data easily accessible to those who need to use it?

In the context of these use cases, metrics are critical to ensure that the data needed to support them is accurate, complete, timely, and consistent. 

Three Tips for Successful IT Modernization

1. Assess Data Health Before IT Modernization Efforts

When undertaking IT modernization initiatives, it’s common practice to cleanse data before moving it into a new platform or application. But more often than not, the cleansing exercise is an afterthought — it’s initiated late in the project timeline and does not allow for the time, budget, and resources needed to adequately assess and cleanse the data. Cleanup efforts require engagement and feedback from business owners and often require investigative work to identify and correct the root causes of data quality issues up- and downstream. If this is not done early and often, data issues create risk for implementation timelines and the integrity of the application itself. So don’t wait; get going on the data early. 

2. Don’t Go It Alone 

Over the course of my career, I have supported many IT modernization initiatives, including MDM, CRM, ERP, and countless other three-letter application acronyms. But the clients I work with are often going through IT modernization initiatives for the first time. They don’t know what they don’t know; they know what they are being asked to do, but they don’t know where to start. This is where I recommend engaging with a third-party data provider such as Dun & Bradstreet for an objective view of your data. 

Working with a third party can save both time and money. Not only will a third party objectively assess the health of your data, but they will also come to the table with pitfalls to avoid and best practices to adopt based on its collective experience of working with many clients. Entity resolution, deduplication, and account aggregation are especially complex concepts for organizations that may not yet even agree on their definition of a customer. Open collaboration, including the sharing of use cases, KPIs, milestones, and budgetary requirements, forges a partnership with shared goals and vested outcomes. In addition to data expertise, third-party providers bring an outside-in perspective and provide expertise on building business cases, organizational data maturity, data integration, design, and data to support your use cases. 

3. Be Sure Your Data Is Fit for Purpose

Having high-quality internal data isn’t enough if the data will not enable your use cases. For example, if you are asked to support a vertical go-to-market strategy based on an industry, you need the industry names or codes that current customers and prospects fall into. While it’s possible to gather the industry information yourself, there are drawbacks. These include resources and time required, the risk of the data being subjectively biased by the people creating it, and it being difficult and costly to maintain. Industry classifications are an off-the-shelf attribute for many third-party data providers and come with instituted data governance standards and maintenance practices. The business case for buying the industry data (or other segmentation, risk, and behavioral data) is the fastest, cheapest, and most objective path to enabling your use cases and setting up your IT projects for success. 

Data is foundational. Whether trying to improve business performance or dealing with a costly disruption, it’s the data — not the tech — that makes the ultimate difference. Starting with, and maintaining, trusted data is the right way to reap ROI from your tech. 

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AMY COOPER, Principal Consultant with the Dun & Bradstreet Data Advisory Services team, is a 30-year veteran in data management. In her career with D&B and with Gartner, Amy has worked with some of the most complex organizations in the world supporting master data initiatives at the enterprise and at the sales, marketing, analytics, supply, and finance functional levels.