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How to Develop a Robust Data Strategy for Digital Transformation

01 Aug 2019


To reap the benefits of technologies such as AI and Machine Learning, it’s critical to start identifying your most useful data, then start connecting your data sources together.  If you haven’t already, it’s time to start mobilising your organisational resources to develop the discipline and tools you need to manage and mine data in the future.

Gartner underline the reasons why it's important to develop your data strategy: 

“Data and analytics are the key accelerant of an organization’s digitization and transformation efforts. Yet today, fewer than 50% of documented corporate strategies mention data and analytics as fundamental components for delivering enterprise value.” *

Developing Your Data Strategy with the Right Foundation

As the volume of data used within business increases, managing it can become overwhelming and extracting insights almost impossible.   And if you think this is challenging now, what will happen when connected-device count really begins to mushroom?

Now is the time to get started and get your data building blocks in place.

In our ebook our data experts provide a succinct overview of how to get those first data building blocks in place. Download their eBook here

How to get your Data in Shape

Start with identifying answers to key questions:

  • What data do you have?
  • How it is working?
  • Where it is stored?
  • How it is accessed?
  • How is it performing against existing platforms?
  • What is the data’s purpose?

Once you have clear answers to these questions you can begin to Identify the best location for your data sets.

Why a Data Assessment is a Crucial First Step

A Data Assessment helps you identify what to keep, what to archive and what to remove.

It helps you agree as an enterprise what constitutes ROT (redundant, obsolete and Trivial) data. It can also help you collect metadata from file shares, then identify the data items considered to be ROT.

Understanding "ROT"

Redundant files are duplicate files containing the same content e.g. copies of documents, emails, records or images.

Obsolete files are those whose business value has reduced over time. For example, if they have not been accessed for 5 years.

And finally, Trivial files are those classified as not company related, for example, audio, video, images, temp and system files


Key Benefits of a Data Assessment

It can quickly help you:

  • Understand how data can be optimised
  • Discover capacities by file type and extension
  • Identify large file owners
  • Obtain a baseline to build a future data life cycle management strategy.

At the end, you should be clear on findings, recommendations and next steps within your data assessment.

Standardising Your Data for Digital Transformation

It’s important to make sure all data objects of the same type follow the same standard and format. Ultimately, your goal is for data formats to be identical in all record sets and clearly understood.

If data formats are inconsistent, you could end up with an incorrect data set when different data-sets are merged.


  • Data should be standardised before consolidation in data storage. (If not possible within the source system: Extract, Transform and Load (ETL) your data.)
  • Develop a Data dictionary or Master Data Model to ensure everyone can see what data standardisation practices are used from each “source” system.
  • Finally, don’t forget to factor in data growth. Most data sets will increase exponentially, year-on-year.

After all, there is no point striving to become a data and insight-driven business if your data is of inadequate quality. 

The old data adage: rubbish-in, rubbish-out has never been more salient. (And would you want to be responsible for making company-wide decisions based on rubbish data!)

How to Consolidate Old and New Data

As part of your initial data strategy you need to consolidate data from operational silos. As part of this, identify data sources vital to your organisation and understand how data is used within operations.

And don’t forget to review your archive data - this could be in long-term cloud storage or a Tier 3 disk within your datacentre.

Once you have the full picture, start to augment company data: with publicly available data, sensor data, 3rd party paid for data, or from the Deep Web.

The more complete, relevant and up-to-date your data set, the greater the probability of extracting insightful actionable results from it.

In the full eBook our experts, Jason Normanton, Tony Muraki-Hart and Will Wilkinson, discuss the further steps you need to follow to create a strong data foundation and data strategy for digital transformation.

We recommend reading their chapters on:


Download our Intelligent Data e-book



* source Gartner - Why Data and Analytics are Key to Digital Transformation 


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