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The Three Challenges to Harnessing Data

The Three Challenges to Harnessing Data

Data has become one of these ubiquitous words that gets mentioned in all sorts of contexts, because when used and applied properly it could prove to be the closest thing to the ‘holy grail’ in all things marketing and beyond.  But, for all the talk that goes on I don’t see many examples where data is being utilised to its full potential, and there are a few key challenges that need to be overcome if we are to get the most out of data and what it can bring.


Firstly there is the issue of the availability of data.  Despite it being the buzz word for the last few years, it can still be incredibly difficult to get as much data as we would like.  Most organisations are still set up in silos with different priorities, so as much as we may have some data from one department it can be very difficult to get data from other departments – data which could give a much better view of their customers/trends in the market to base long term strategy on.  Organisations need to start realising that by sharing more information and data it will benefit everyone in one way or another.


Secondly there is the quality of data.  Given that people have been collecting data for years prior to technology enabling us to make sense of it all, the data storage infrastructure may not be fit for purpose for what we need today - hence the information we get out of it could be very minimal.  Without knowing the full extent of what kind of ROI you will get from upgrading it to something usable it may be difficult to change these systems, but it should be an absolute priority for any company looking to grow their business with smart data fuelling everything that they do. 


Thirdly we should be tracking and collecting as much data as possible with whatever activity or engagement we have with consumers.  Too often we get carried away by a ‘big idea’ and don’t do the due diligence behind what kind of tracking and data collection we need to do to make sure that we can get as many learnings as possible and ensure whatever data we get is robust enough.  This obviously includes both micro and macro factors too as this could have a huge implications on the results that are generated.

Without taking these three things seriously we are basing all our future strategies on just a small snapshot of information, which could lead to wrong conclusions and could potentially be detrimental to your business.  Yes, these changes may take some time and yes, it could involve restructures and big changes in the ways that we work – and we all know that change can be very disruptive. It could also incur incremental costs if systems and infrastructure need to be replaced. However, the more important question should be around the cost of not doing this. By standing still within this landscape of change how much will it cost you in the future? I would argue that the opportunity cost of carrying on as normal is far greater than the cost of getting your data aligned.

And it doesn’t have to happen overnight.  We know it will take time to get to the ideal position you want to be in the data space, so just start by considering what kind of expertise and development is required for your needs - as this will ultimately dictate the trajectory of big data and its use. Simplifying the stage of adopting data usage and combining with good usage cases will yield the best results short term while the dust settles on how the next big algorithm dictates the future. The complexity isn’t going to get any easier and the need to solve this complexity is still as prominent as ever, so it’s necessary to shift the model from influencing the consumer through broad context to a now real time personal context. Work to simple rules of:

  1. Start with what you think you know of your customer and use data to validate or create new learnings.
  2. Develop your data touchpoints. What do I currently capture, what else could we capture with further development?
  3. How can this influence our decisions and action from our marketing strategy to product development? Data analysis will inevitably create new thinking and new approaches but the supply chain of change can be long so why run before you can walk when the path is still unclear on the big winners and losers.
  4. Get out there and start working with your existing relationships because it’s a challenge best tackled together.

Whilst there is a lot more we can do this is a good starting point as any for any clients starting to consider their data options, and this should lead to the aforementioned 3 points becoming easier to address as organisations start to see real benefits from using data properly.  And the more we can learn the quicker we can adapt to whatever the landscape might throw us in the weeks, months and years to come. 

Takako Elliot

Takako Elliot

I joined Mindshare as a graduate in 2002, and in my time at Mindshare I've worked across all market sectors including Retail, Finance, FMCG, Telcos and Auto clients, acquiring a broad range of digital skills along the way. My current focus is on driving clients’ business forward through data, media and technology innovations as well as ensuring that we deliver the most efficient cross media campaigns across all consumer touchpoints.

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