Turn Big Data into True Business Benefit

Every day, we create 2.5 quintillion bytes of data – so much that 90% of data in the world today has been created in the last two years alone. When data sets get so big that they cannot be analysed by traditional data processing application tools, we have ‘Big Data’. Today, Big Data are woven into every sector, used to creating transparency, enabling experimentation, improving innovating, supporting decisions, increasing segmenting, etc.

July 2017

However, although organisations may hold an enormous amount of data, they’re not always using it to its full advantage. All organisations need to take Big Data and its potential to create business value seriously. It is the path to competitive advantage.

Curate Data for Deeper Insights

In practice, the term Big Data often refers to the use of predictive analytics, user behaviour analytics, or certain other advanced data analytic methods that extract value from data. Very large data sets are analysed to reveal patterns and trends and find new correlations to spot business trends, prevent diseases, combat crime – enlightening the path to true benefits. Data integration requires much more than simply collecting skills. Similar to the curator of a major Vincent van Gogh or Pablo Picasso exhibit, data curators need to follow some simple steps to elevate their work from good to great, to generate unique perspectives and insights and to tell a story that resonates.

  1. Know your objective from the start. It may seem obvious and yet it is a common mistake many companies make. Having a clear picture of what you want to achieve will help separate data that is truly useful from what is simply available. Don’t use opportunism as your guiding principle.
  2. See the bigger picture by thinking of implementation. The data sets are less important than the insights you are looking to activate and your reason for activation. Think about how your integrated data set will be applied by your business. Plan your analyses knowing what decisions need to be made, who is deciding and how the decisions will be made. Drive change, don’t just develop insights.
  3. Be directive, not dogmatic. Be careful not to replace surveys and other custom solutions with data integration. Rather, combine the two. Understand your data, use it and look to fill in the most important blanks with additional survey insights. Follow the insight from the data.
  4. Be additive, not duplicative. Look for data sources that complement rather than overlap. Having two different metrics for the same market dynamic is confusing, not constructive. Good curation requires looking beyond the options handed to you. Find the right yin to your existing data’s yang.
  5. Embrace transparency. Do not accept ambiguity in your data sources and avoid it in your own output. Understand the quality and limitations of your data sources before you bring them together and look for better data if you are not satisfied. Also, be very clear about how you have combined and edited information, so it’s clear what it is and how it can and cannot be used. Black box approaches ultimately benefit no one.
  6. Understand your audiences. Understand the decisions that users of your data need to make and put the right data front and centre in the dashboard. Putting one insight next to another may bring out new implications in both, just as hanging two paintings side by side reveals unseen elements in each. Make sure results are presented in ways that are useful.
  7. Be data-agnostic. If the project’s goals require the incorporation of third-party data, publicly available sources and the client’s loyalty card records, do not resist the obvious. Predetermined ideas about where proper insights come from and who is profiting can distract you from your top priority: meeting the client’s stated needs. Nimbleness and flexibility are essential qualities of the data curator.

Sources: Computer Business Review (2017) What is Big Data and why is it important? [ONLINE] Available at: [Accessed 20 June 2017]; Janssen, J., Van der Hoort, H., and Wahaudy, A. (2016) Factors influencing Big Data decision-making quality, JBR, 70 · August 2016; Data, data everywhere. The Economist. 25 February 2010. Retrieved 9 December 2012; Amarican Marketing Association (2017) 8 Tips to Curate Data for Deeper Insights and Better Decision-Making. [ONLINE] Available at: [Accessed 20 June 2017].

Referenced techniques


Big Data Analytics

Big Data analytics is complex, in order to succeed organisations need to invest in the people behind the technology. Strengths and weaknesses are considered and practical case studies of implementation are shared to help organisations build up their Big Data capabilities.


Business Intelligence

The theory reviews the notion of Business Intelligence and discusses the conditions necessary to make good decisions. It also explains how it helps organisations to unlock the hidden potential of data.


Database Systems and Management

The full potential of a database system will only be realised if it is properly planned and developed. Practical case evidence is provided alongside advice for how organisations can effectively move from concept to design to implementation.

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