Making the big data big decision

In God we trust, all others bring data.” (William Edwards Deming, American statistician, professor, author, lecturer and consultant).

Although my wife and kids hate this quote from Deming, it epitomises the struggle every business decision-maker is going through today. Our social infrastructure is changing in unperceived ways. There were times (even as recent as my father’s) when everyone knew everyone else within their physical radius of operation. Bank managers knew their customers by name and shopkeepers knew exactly what each one of the customers asked for in their monthly groceries.

As cities grew and became metropolises, managing information was becoming difficult enough – and then digital happened. Physical boundaries have become irrelevant and the influx of information has grown at an exponential rate so that managing it, let alone processing it, is a huge challenge.

Forget about trying to figure out trends; according to IBM, 2.5 billion gigabytes of data was being generated every single day in 2012. Today, digital natives can individually generate 1.7 megabytes of data every second. Process that if you will. Now imagine the size of that data literally doubling every single year. The physical devices needed to store this have become the challenge.

Data and analysing trends from data have always been key for businesses that want to have an edge over competition. As the size of the databases grew, Big Data (BD) came into being – which is simply the analysis of huge amounts of data spread over multiple locations or devices.

Adding to the complexity, the data is a mixed bag of structured and unstructured information.

For example, Visa globally compiles exabytes (one billion gigabyte) of data every single year. This includes both structured data, such as transaction history, and unstructured data, such as customer behaviour on social media. Using BD tools, Visa were able to accurately predict the demand for services and even fraud patterns – and needless to say, the results have helped the company improve bottom line results. According to Forbes, 87% of companies today believe that BD will have a significant impact on their industries before the decade is up. They also believe that not having a BD strategy will be a major handicap in the future.

Nine out of 10 business leaders consider data to be as fundamental to business success as land, labour and capital. According to research, on average, improvement in business performance because of BD is 26%; a figure that is estimated to grow to 41% over the next three years. Imagine having a 41% impact on your bottom line; that is almost doubling your revenue or margins.

In Pakistan we still haven’t quite come around to understanding the importance of this treasure. At the moment, the information is sitting there waiting for someone brave enough to invest in BD and mine the gold. I know of at least a couple of banks that are already formulating strategies, but by and large, you will be surprised at the lack of interest, especially among local banks.

Another point that needs to be clarified is the difference between BD, data science and data analytics. I am surprised by the number of business and marketing people who don’t know the difference. I blame the agencies for this. It is just not in the digital agency culture in Pakistan to educate clients (exceptions are there, of course), instead, we tend to focus on selling solutions only – and especially in areas that we are good at.

Data scientists are among some of the highest paid people in the world and data science is generally applied in internet search, search recommenders and digital advertisements.

So here goes. BD is used to analyse insights to make better business decisions. Initially BD was used to refer to the data itself but has evolved to include the process and tools required to analyse. BD is generally used in the financial, retail and communication sectors. Data science consists of data cleansing, preparation and analysis. Data scientists are among some of the highest paid people in the world and data science is generally applied in internet search, search recommenders and digital advertisements. Data analytics involves automating insights into data sets and using queries and data aggregation procedures. Data analytics is primarily applied in healthcare, gaming, travel and energy management.

Coming back to BD, the financial sector is not the only one benefitting. Retail is a strong second contender, with BD applied in every step of the retail process to predict trends in product demand, which in turn, helps manage supplies. Furthermore, retailers can optimise pricing to gain a competitive edge. Crawlers (programmes designed to search the web and extract data) can identify the price points at which the entire market is offering a particular product/ brand/ model.

Using unstructured data through social platforms, brands can identify those customers likely to be interested in a particular product and then work out the best way to approach them. They can even figure out what to sell based on past and current buying habits. It’s like having a perpetual research agency residing in your laptop feeding you with trends to make better business decisions. For example, using BD, Macy gained a 10% increase in sales. A McKinsey research of more than 250 companies over a five-year period revealed that companies that put BD at the centre of their sales and marketing decisions improved their marketing Return On Investment (ROI) by 15 to 20%.

Another example is of the Pizza Hut chain which monitored bad weather and power outages data. By offering special promotions to customers living in those areas (they knew they would be facing problems), they increased the response rate by 20%. Isn’t science lovely?

Yet another example is the retail store Target. Target took two separate data sets, one from their registered users and the second from their guest users. By analysing trends from the first set they predicted what the second set would demand. In this case, they discovered that registered pregnant women during the first 20 weeks bought supplements (calcium, magnesium and zinc) before moving on to buying a different set of items in their second trimester. So, if any guest user displayed the same trend as registered users in their first trimester, Target then also offered them products frequently purchased in the second trimester, such as large jeans, hand sanitisers, unscented lotions, fragrance-free soap and cotton balls. In doing so,Target grew their retail business from $44 billion in 2002 to $67 billion in 2010 – a 50% increase in business through revenue streams that were not possible before BD.

To sum up, the way we analyse and do business is evolving at a fast pace. In Pakistan, we may think that technology and its impact will reach us at a slower pace, but it is the other way around; countries adapting technologies later in the game tend to adapt the latest first.

This is why Pakistan’s cellular systems are much more advanced than those in certain parts of the US and Europe.

BD is here and it is up to each company to decide how quickly and with what scope they want to start the adoption process. Businesses are benefitting globally and the thing one wants to avoid is waking up five years later and regreting not having adopted digital advances to grow one’s business. The market is a very unforgiving place.

Amir Haleem is CEO, Kueball Digital. syedamirhaleem@gmail.com