Getting the most out of your data and mitigating data-related risks relies on having a strong data governance strategy in place to address both current and long-term business objectives.
Given that 80 percent or more of corporate data is unstructured – meaning it cannot be readily parsed or utilized – a strong data governance strategy is no longer simply a “nice-to-have.” Instead, it presents a wake-up call to businesses that it’s time to get serious about data governance.
Data governance refers to an organization’s overall ability to manage its data so that it’s not only accessible, but usable, secure, and of sufficient quality to drive actual results.
Implementing an end-to-end data governance strategy has become a business imperative that enables enterprises to gain insight into their data stores to reduce any PII compliance risks and further enrich data so it can be leveraged to accelerate new product and service development, improve the customer experience, and fuel process automation.
Whether you have an existing data governance strategy in place or are just beginning to think about your organization’s ability to enhance data quality and availability, here are 6 fundamental principles that should inform your approach.
If you are still in the early stages of thinking about your organization’s data or are further along in your digital transformation journey, a data governance strategy hinges upon knowing what data you have and ensuring that it exists in machine-ready, usable formats. In other words, your data must be easily and readily accessible. This means that regardless of the type of data your files contain or what you want to achieve with it, you must take steps to ensure that it is ready to go when you are.
This involves converting data that is not already digitally accessible (for example, paper documents, mixed-format files, and images) into machine-readable formats. Once your content is in a searchable, text-based format, it should then be appropriately classified so that you know what you have and can easily access what you’re looking for when you need it most.
An Alberta-based energy company implemented Adlib to convert all of their oil and gas paper records into digital format. Their main objective was to comply with industry regulations regarding document control. Using industry-leading optical character recognition (OCR), Adlib converted all scanned TIFF files into full-text, searchable PDF documents. In addition, Adlib integrated with the company's product lifecycle management (PLM) platform, significantly improving data accessibility and staff productivity when it came to records management.
The old saying “garbage in, garbage out” is especially relevant to data governance.
Whether your objective is to leverage data for better business insights, improve productivity via automation, or another goal, your results depend on your data quality.
Simply put, if your data contains redundancies, errors, outdated information, or other problems, these issues will impact your outputs.
Poor data quality is a significant issue in data governance: it’s estimated that 69 percent of corporate content is ROT (redundant, obsolete, or trivial information). So, to achieve quality results from any data-driven initiative, it’s crucial that enterprises take steps to illuminate and filter out low-quality data so they can instead focus on enriching the information that matters.
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Strong data governance is not only about being able to use your data today, it also involves planning and undertaking steps to preserve important content for the future. In the past, data preservation was a massive undertaking: when information existed primarily on paper, archives required massive amounts of physical storage. Keeping track of what was within these content stores was challenging, if not impossible, especially as repositories grew. It was also impossible to maintain the integrity of data as physically stored documents are prone to deterioration over time.
To harness the power of deep legacy information and safeguard important documents in the event of a disaster, companies must make preservation a key pillar of their data governance strategy, ensuring that documents have been digitized, categorized, and made searchable for future, long-term retrieval.
With the growing prevalence of data breaches – and the massive costs and reputational damage a breach can inflict – data management carries inherent business risks that must be addressed in a data governance strategy. These risks are typically connected to sensitive information contained within your files, such as those pertaining to PII compliance.
To mitigate these risks, you need to first know what risky data you have in your possession and where it resides. Only then can you take steps to protect this information, ensuring that documents containing personal information are archived or destroyed according to PII compliance policies, or other actions.
From customer onboarding forms and transaction records to employee documents, a business’s data spans across several teams, business lines, and locations – and so must your data governance strategy.
Data quality and accessibility rely on taking a holistic approach, ensuring consistent and company-wide strategies and processes as to how data is formatted, organized, and stored.
In the absence of a holistic approach, data can become siloed, meaning critical information is locked within locations where it cannot be leveraged to its fullest potential.
As you consider your plans to achieve each of the above principles, it’s important to think about the sustainability and scalability of your tactics.
With growing content stores, data governance is not a one-time consideration – your data governance strategy needs to encompass all the data you have now, and all of the data you will create moving forward. This means not only planning for how you can improve the accessibility, quality, and other essential attributes of your existing data, but ensuring that you can efficiently replicate these tactics as your content stores grow.
Getting the most out of your data and mitigating data-related risks relies on having a strong data governance strategy, both now and for the long term. Whether you’re at the beginning of your journey or are looking to improve the results of existing data-driven initiatives, Adlib can help you achieve efficient, scalable results that meet all the principles of strong data governance.
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