Jason Hardy

Stairway to Value: Finding Value Through Enrichment

Blog Post created by Jason Hardy Employee on May 25, 2018

We are all inundated with data – every day, in every way, and from many different sources. The reality of this is simple – there’s no end in sight for the exponential growth of data. Just look at the many sources that point to growth trends.  But what does it really mean – in a way nothing (noisy data producers are just that – chatty), and in other ways it means everything!  So much of what we do today is big data stemming from deep data, and it has been this way long before either term has been around.  Your goal is to help your business not just with the tools you have, but with the knowledge you glean from the relevant data at your disposal.  In other words, in and of itself data has no intrinsic value – it’s only of value if it supports the effective execution of the business function calling on it.


This is where we (Hitachi Vantara) are here to partner with you in an effort to make every bit of your data available to you, with as much insight as possible, thus enabling you to take some kind of informed action.  If I look specifically over the last year at the different customer interactions I have had on this topic, it quickly becomes apparent that data growth is just one of (6) common challenges faced by organizations like yours.


Data Growth Challenges

  1. Data growth is how I opened this blog, let us agree once and for all that it will always be growing
  2. Organizations don’t often realize there is a problem with their data in advance of a situation.  Reacting creates in-the-moment data management approach that fails to scale as the business grows
  3. Lack of processes, data management systems, and inadequate data strategies contribute toward data quality issues and untrustworthy data sets
  4. Lack of data ownership is a key shortfall for most organizations.  This fragmentation is often across different departments and stakeholders, versus the business as a whole
  5. Many organizations are unable to invoke enough support to improve their data culture.  This is often driven by a lack of knowledge or skillset on the proper techniques to manage data effectively
  6. Items 1-5 quickly become catalysts that foster single-purpose solution adoption that ultimately traps data in technology silos that ultimately increase compliance and productivity risks for the business


The problem with data growth isn’t the velocity or complexity behind it – it’s the innate human nature to store it – all of it.  The running joke being that no one has ever gotten fired for keeping data.  But are you gleaning the benefits from this data you’ve stored?  It’s not about what data you’re storing that is of value to the business, the real value and benefit is what you are doing to augment the data to drive what I call “continuous digital viability”.  This is the value of data enrichment because it helps you to drive a better understanding and intelligence into your business which helps improve decisions, stimulates customer engagement, and improves your bottom line.  Simply put, data enrichment is about improving data quality.


Data Enrichment

Your ultimate goal should be to boost the data that you are currently storing. Whether it is at the point of capture or after the data is accumulated.  I would argue that moving the focus of data quality upstream closer to where it originates and is embedded into the business process is better than trying to catch flawed data downstream and fixing it in all the various applications that use it – more on that in another blog post.


Data enrichment is the crucial step necessary to improve data quality and ultimately transforming raw data into business value.


Data enrichment can take on many forms depending upon who you ask.  As we engage with organizations like yours, we are seeing a common trend develop in how this is performed:



Aggregate: collate disparate data sources by identifying, linking or merging related entries within or across repositories

Cleanse: amend, remove or enrich data that is incorrect or incomplete.  This includes correction, standardization, normalization and enrichment

Profile: analysis of the data source to provide insight into the quality of the data and help to identify data quality issues

Monitor: continuously tracking and monitoring the state of data quality and the need to further enrich data to refine its true value to the business function or purpose of use.



At Hitachi Vantara, we do see data as your most strategic business asset and continue to have the fortune of helping organizations achieve higher degrees of data enrichment, quality, management, governance, and mobility with our data services platform.


As organizations increasingly rely on analytics and business intelligence to deliver innovation and value, they must take a practical and fit-for-purpose approach to how data quality is addressed, how data is managed and governed, how it is mobilized, and how it is controlled.  As your partner, Hitachi Vantara’s Data Intelligence team can help you ensure your organizational success is based on data of the highest referential value and quality is the foundation for any and all analytics techniques supporting your operations.


Collaboration Credits

Co-Author: Scott Baker

Co-Author: Peter Sjoberg

Co-Author: Nishant Kohli