High-performing organizations rely on business and customer data to guide, operate, and improve their business. But as we have learned recently with forecast and customer expectation missteps by companies such as Disney, Netflix, and others, your data is only as good as your ability to act on the insights made possible by a modern data governance strategy. Without data governance, improper data management and access practices act as the business world’s second-hand cigarette smoke — lurking and waiting to strangle your business — and customer delight along with it.
Today, accurate, actionable, and accessible data is required to compete in an environment where customers (and prospective customers) expect instantaneous information, informed responses, and more value, and your teams (as well as the growing number of automated machines and artificial intelligence algorithms) require access to quality data to make and act on decisions.
None of this is possible — at scale — without a company-wide, well-documented, defined, and implemented data governance strategy. In today’s business environment, the stakes are too high to pay lip service or approach data governance as a nice-to-have priority. In fact, according to Deloitte, the lack of data governance discipline cost U.S. companies an estimated $3.1 trillion in 2022.
To help you address this business imperative, this data modernization analysis provides:
- an understanding of the role of data governance in your business today
- an outline of the essential ingredients of an effective data governance strategy and policy
- a high-level road map to ensure data governance success within your organization
The Why: Data Governance’s Business, Customer Impact
Data governance is a set of processes, policies, and strategies that define how an organization handles data management. Data governance’s goal is to ensure that data is accurate, compliant, accessible, and secure.
Governance must be applied to a wide range of data types, including customer, buyer, product, operational, financial, and market data. And governance must cover first-party data from many sources, including data pulled both from an organization’s internal systems and databases, as well as data used by third-party sources to augment internal data, such as data brokers, data bureaus, and business partners. Just as importantly, an effective data governance policy also establishes who is responsible for data under specific circumstances.
Data governance can help mitigate:
- Inaccurate or incomplete customer data (wrong contact, bad product info, incorrect purchase history) that quickly turns into lost or disgruntled customers when your company can’t confidently or consistently deliver on their needs
- Fragmented or missing buyer behavior data that hampers your ability to predictably present the right product or offer at the right time in the right format to customers, or insights to key employees or business partners who rely on intelligence to conduct business
- Data management that violates specific data privacy laws and regulations
Bottom line: lack of strict data governance equals lost revenue and profit.
Lastly, the increasing use of artificial intelligence (AI) and automation is escalating the need for more stringent data governance practices. AI introduces more complexities and will be the topic of future articles on this topic from me and from the rest of the Acceleration Economy team.
The How: Essentials of Data Governance
With an understanding of what’s at stake, let’s lay out the critical components that make up an effective data governance strategy and policy. Defining and documenting the ingredients and aligned processes and policies will enable you to use data and apply intelligence in your business confidently and consistently.
- Data management + ownership — Defining how, where, and who manages, handles, and stores data is at the core of data governance. Assigning accountability for the specific roles and functions that are responsible for data security, availability, collection, usability, integrity, compliance, and systems is key to an effective governance strategy.
- Data access + security — Data comes from different internal and external sources and systems, so it’s critical that there be a clear policy on securely storing and accessing specific data types. Easy to say, very hard to do. Stringent security tech, policies, and processes to prevent data breaches and misuse must be balanced with providing access to empower employees and machines to be able to put data to work to advance the business.
- Data accuracy + integrity — Data is only as useful as its accuracy and completeness. If leaders can’t trust data quality and integrity, not only will data not be used to its potential, but worse, it will lead to bad decisions and poor customer experiences, crippling business. And because data — especially of the behavioral, buyer, and market variety — has a shelf life, the governance policy should have a clear plan to deal with data expiration and updates.
- Data integration + structure — Data is stored and resides in a variety of internal and external systems and databases. Without standard, automated application programming interfaces (APIs) and intake processes, as well as an ability to integrate and aggregate data sources and systems, data silos and manual work will become the norm. Fragmented, unstructured data creates accuracy and actionability problems, and it can limit your ability to use it effectively and efficiently.
- Data compliance + privacy — Fines and brand reputation are just the start of the challenges if your data governance does not respect and protect customer and personal identifiable information data (PII) such as social security number, taxpayer ID, and credit card information. Lost trust and confidence in doing business with your company is a compounding problem that is hard to overcome, often for years, and it can cost your business millions of dollars.
The Now: Modernizing Data Governance
As I’ve explained above, data governance has lots of moving parts and complexities in the typical organization. As with all modernization efforts, it’s best to craft a plan to accelerate impact, starting with clear data governance goals, a thorough assessment of your current state, and desired outcomes.
To close, I’ll share three big lessons learned from data governance projects with which I was involved.
First: make sure you make a strong business case to secure the resources and investment required to get your data right. The information outlined in this article can be a starting point to make the case of why and how.
Second: make sure you make data governance and integrity part of your culture. Reward and highlight stand-out work and innovation using examples where accurate and actionable data is generating revenue, creating efficiencies, and delighting customers.
Third: share the good, the bad, and the ugly throughout the data governance modernization effort to educate leadership and stakeholders. Transparency of progress, using actual data, sources, and types, as well as what you are learning, builds credibility and confidence, which further builds momentum and good governance habits.
In the end, our ultimate goal is for data governance to be an essential part of the way business gets done.
Which companies are the most important vendors in data modernization? Click here to see the Acceleration Economy Top 10 Data Modernization Short List, as selected by our expert team of practitioner analysts.
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