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The Big Data phenomenon is broken…

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Until you can get full value from it!

data newererWhen it comes to ‘Big Data’ one size definitely does not fit all. There’s a right and a wrong way to do data analytics.

The term big data is now so over-used and vague it has become almost meaningless. Companies are taking existing technologies and trying to convert that technology into delivering analytics and end up using the wrong tool for the job. We live in a dynamic and rapidly changing world and people want to use analytics to improve how they’re doing business so a data tool needs to be able to adapt and change quickly.

The key to a successful big data project is for a business, regardless of how big or small it is, to start from the desired business outcome. Once you know what you’re trying to get out of the data, you can then decide what’s the best analytical tool for the job.

Too many big data projects start by searching for the technology, dumping a mass of data into it and only then try to work out how it can help the business.

Some retailers are trying to track customers moving round their stores via their mobile phones and RFID tags. They’re putting ‘beacons’ on products and shelves and using their RFID tags to pick up a mobile phone signal. Consequently, one of the excuses for failure of RFID projects was that they were swamped by the data.

But this is a poor excuse. These projects are still failing even though retailers have numerous big data tools to choose from that purport to be perfectly capable of handling the data volumes. The problem lies in assigning meaning to data. It’s both an intellectual and a technical problem.

The human factor can also complicate big data. Analysing text in CRM and social media can be difficult because it involves interpreting customer opinions and deciding whether they are positive or negative about a brand. Computers don’t do irony.

So, how can companies get bigger benefits from their data analysis?

Here are five tips:

1. The business has to say what it wants to achieve from collating and analysing data. It should lead the project; not IT whose staff can feel threatened by big data.

2. Don’t ask questions that can’t be answered by the data. Retailers may want to know how many repeat visits customers make. But if, say, 40% of their sales are cash sales rather than those made on debit or credit cards that’s a big gap in sales data. Some retailers will struggle to identify individual customers in stores or customers with different cards.

3. Start small and then get bigger. Cut irrelevant data. If you’re trying to understand all customers, do you need demographic data on them? It may not be accurate. Do you need your trial to use data on all customers or just use data from 5 or 6 stores?

Bear in mind that Tesco trialled its loyalty card in a handful of stores. When the value of the data was clear it rolled out the system across its business.

4. Manage your costs. Pick suppliers who allow you to trial technology and will help you understand the technology.

5. Understand what data really matters to your business. For retailers the most common objective is to sell more products, at a better price, more often.

Another common goal will be a more efficient supply chain. Be specific. The aim may be, for example, to use your data to work out how to get stock on your shelves faster; or get more short-sleeved shirts on shelves rather than scarfs on a hot day.

Having a clear business outcome makes it easier to measure the value of your data project. Yes, much of these tips are common sense. But in the rush to use big data companies are being dazzled by technology and forgetting that it should serve the business. Not the other way round.

 

Laurence Armiger

Laurence Armiger

Laurence Armiger is Sales Director for Zizo where he responsible for working with customers to deliver business benefits by quickly transforming raw data into actionable information. Laurence believes that businesses of all sizes should have the ability to access and use data to improve their business, without the costly investment associated with traditional big data solutions.

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Tags: Data Center, Technology