Tackling Key Digital Transformation Challenges with Better Data

Posted 4 February 2019 1:29 PM by Jason Mitrow
 

It’s hard to understate the extent to which we’re becoming a data-driven society. Whether it’s automating manual tasks or analyzing historical trends to gain new insights into customer needs, businesses are using data to improve process efficiency, decision-making and drive better overall results.

The potential of digital transformation in the enterprise is vast – and so is the importance of data quality in driving these business outcomes.

But in order to tackle key challenges of digital transformation and ensure your enterprise data can help you meet your end goals, it’s important to understand how poor data quality could be hindering your results.

Why dark data is bad data

It’s a stat you might have heard before: as much as 80 percent of data is unstructured. But what does unstructured data mean? Also called “dark” data, unstructured data refers to content that is locked in formats computers cannot readily access.

Dark data may exist within paper files, file shares and emails that contain scanned documents or images, duplicate files etc. There are many reasons dark data exists; often it may be due to legacy processes and systems, or poor adoption of a data governance strategy.

Whatever the cause, the end result is the same:

Unstructured data is essentially unusable and contributes significantly to the key challenges of digital transformation.

Whether your digital transformation project involves implementing automation, artificial intelligence systems or other technologies to enhance process efficiency and improve customer experiences, enterprises need a consistent flow of high-quality data to propel their efforts.

What does ‘better data’ look like?

If poor data quality is a prominent, key challenge of digital transformation that businesses need to overcome, then it follows that companies simply require better data to drive results. But what does “better data” even look like? Here’s a breakdown of the three pillars of data quality that can fuel digital transformation success:

Better data is accessible

As discussed, the problem with dark or unstructured data is that machines can’t typically read it – and from machine learning algorithms to robotic process automation (RPA), digital transformation initiatives rely on computers’ ability to search and read information assets, and utilize the data they contain.

As such, the first step to achieving better data is to standardize content to ensure it’s accessible – in other words, it must exist in universal formats that can be processed by computers. Improving the accessibility of documents also involves data extraction. Once the documents are seamlessly converted into a useable format, organizations can employ a data extraction process to unlock the content within each file.

While companies may possess some amount of structured data that’s fueling their decision making, the majority of it likely remains in unstructured formats. Any journey towards digital transformation hinges on having uniformly accessible content, made possible by standardizing content, employing data extraction and further converting any unreadable information into searchable, text-based documents.

Better data is relevant

Having accessible data doesn’t always mean it’s automatically useful. Documents can contain data that is outdated, erroneous or redundant, or may contain other issues that can skew your results. The next step to combat this key challenge of digital transformation involves ensuring data is relevant by weeding out the 69 percent of content that is ROT – redundant, obsolete and trivial.

To make this part of data enrichment less onerous, businesses can leverage AI-based classification technologies, which utilize machine learning to analyze the documents, identify and organize their content, and eliminate ROT. When organizations can see their data more clearly, they’ll be able to make more informed business decisions and leverage their content to fuel innovative technologies and process efficiency as part of their digital transformation initiatives.

Better data is timely

To drive ongoing business results – and further accelerate process efficiency – you have to be able to consistently increase your data quality. In other words, better data isn’t a one-shot deal – it’s an ongoing process that allows you to leverage both legacy and newly created content as you need it.

Ensuring you have the processes in place to guarantee accessible, relevant data on an ongoing basis will help to resolve the key challenges of digital transformation and improve the results of future digital endeavours as well.

Wrap up

If you’re struggling to see results from your digital transformation initiatives, it’s likely that poor data is to blame. To overcome the key challenges of digital transformation, businesses must embrace the three pillars of better data, ensuring their data is accessible, relevant and timely.

But, before you can optimize your data quality, it’s essential to understand what content you’re working with. Adlib’s Data Discovery Assessment will kick-start this process towards actionable intelligence by first shining a light on your dark and dormant data before you undertake any unstructured data analytics. Click here to schedule an assessment as the first step in tackling the key challenge of digital transformation.

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