What is Big Data Analytics? The Elephant in the Boardroom.

Big data is the the elephant in the boardroom. Companies know it’s important but most just don’t know what to do with it. According to a study of over 1,600 businesses, an astounding 76 percent of businesses lack the understanding of the potential value of their information and therefore had not invested in the data management platforms and data management software to help them use their data strategically.; Understanding what big data really is; puts some companies at a disadvantage since they do not see the importance or future of this in its entirety.; Big data is the overwhelming tidal wave of data that is collected with every click of the mouse, swipe of the credit card or like in social media. Data is continuously being collected about what customers like, what they do and how they use the internet.; This data can be analysed to increase sales, improve service, and break down information barriers. Big data is increasingly prescient in our lives. It’s used by Netflix to determine viewing preferences and geographic areas to improve streaming. Online news outlets use big data to recommend stories tailored to your interests. Big data is used by retailers to predict future needs. In medicine, big data can be used to create a complete medical history to be accessed across clinics and hospitals, analyse doctor results, or track infectious diseases. Law enforcement can employ big data to cross-reference arrest lists, motor vehicle records, and outstanding warrants not only in-state but across the nation. Big data in politics can combine lists of registered voters, supporters, donors, and potential volunteers in order to mobilize actions to impact the campaign. By combining weather data sets across a geographic region, weather prediction and climate monitoring becomes more accurate. It’s almost not possible to describe all the ways big data can share important information, and improve service.

Big data is a collection of data sets. This data comes from data your organisation has collected internally such as email addresses, customer loyalty information, purchases, and transaction records as well as social media interactions with your brand. This data can also comes from outside sources: web behaviour, public records, and purchased lists which can give information ranging from new homes purchased or new business licenses to credit ratings and purchase records. Looked at as a set and cross-referenced, this data becomes “big data,” a source for analysis and prediction about customer preference and needs.

In the past, companies collected transaction details, customer contact information, and possibly email addresses. They’d mail, email, or even telephone about a generic sale or new product offering. The good salespeople would keep notes about their customers’ preferences and keep in contact.

In the age of big data, companies can use customer loyalty numbers or IP addresses to collect information on products viewed, products purchased, and general shopping and web browsing habits. This data can be used to determine the customer’s stage in the buying process and to make future recommendations. By purchasing other data, retailers can discover credit history, homeownership status, ethnicity, online discussion habits, educational attainment, and purchasing preferences.

At the heart of combining and analysing data are data analysts and data management software. By comparing and analysing sets of data, companies are able to discover patterns in behaviour to inform marketing strategy.

Implementing strategies based on big data analysis can improve sales margins between 8 and 25 percent. Hatami, Vander Ark, and Baumgartner, in their book Sales Growth: Five Proven Strategies from the World’s Sales Leaders, show that implementing practices informed by big data improves sales between 2 and 8 percent in each of the following areas:

• hone the multichannel experience,
• improve cross-selling,
• increase placement optimisation,
• capitalise on location-based selling,
• inform assortment optimisation, and
• refine in-store behaviour analysis.

The combined improvements in each of the six areas combined have resulted in the increased sales rates between 8 and 25 percent, a significant motivator for enacting big data practices.

While most companies have the data to begin using it strategically, just 4 percent of 1,600 businesses surveyed had data strategies and data governance measures in place to realise the commercial and operational benefits of their data.

What is big data analytics? Big data analytics examines large sets of a variety of data to discover hidden patterns, correlations, customer preferences, and market trends.

The following are steps to help your company use big data analytics can help your company grow:

1. Define your company goals first.

Don’t even attempt to implement a big data initiative without first defining an overall objective. Improving your win rate and bottom line is assumed, but focus on the specific hows. Do you want to decrease the cart abandon rate? Target new homeowners? Predict businesses poised for growth? Suggest custom products to an emerging market?

Identifying specific objectives will determine how you approach your big data analytics.

2. Start with the data in your system.

You probably have a wealth of data already, so a great place to start with big data is with the data already existing in your system. Now is a good time to make sure all data is in one place. A CRM system is an excellent way to collect and store data.

Your company is likely sitting on a wealth of data already, write Jeanne W. Ross, Cynthia M. Beath, and Anne Quaadgras in the Harvard Business Review. Most companies, they report, “have not yet cashed in on the information those platforms make available.” CRM reports can be used to analyse your existing data to inform your objectives. For instance, running a report on all X products purchased by time period and location can show you trends. A region that has purchased X product more than other regions would warrant more attention and effort during the said time period.

3. Establish a data-protective culture.

Provide the marketers and decision makers in your company with the data and encourage decisions to be made based on the reports you have at your fingertips. Look for patterns and fill gaps in the information needed to make better decisions on a daily basis.

Protect the accuracy of your data by keeping data updated and making accurate data everyone’s responsibility. According to studies by Ovum, Poor quality data costs businesses around $700 billion a year, or 30 percent of the average company’s revenue. When implementing big data analytics, it’s essential to protect the accuracy of data. Cloud-based CRM software is ideal to ensure everyone in the company has the same data, updated in real-time.

4. Invest in the right people and tools.

As your company becomes more aware of how big data can be used to meet objectives, match your goals with an investment in data analysts and data analytic tools. More complicated data analytics requires experts in predictive data analysis.

Realise timing is everything. As you discover trends and opportunities, be able to act. Drive information to the level where decisions are made and action taken.

Big data analytics is the effort to analyse the data to identify trends and opportunities and get the information into the hands of the people who can take action. While big data promises big rewards to those who can harness its power, implementing big data needs to be done with clear goals and direction. Big data analytics is the sorting and sifting of the data to provide the right information at the right time to the right people in the right place. Decisions about what data to gather and analyse can be critical to the success of big data initiatives. Much time and money can be wasted chasing big data initiatives that don’t pan out if the rest of the organisation is not on board.

Big data is a lot like the story of the 6 blind men and the elephant. Each of them touched part of the elephant: the tusks, the tail, the side, the legs, the trunk, and the ear and each was convinced their perspective of what an elephant looked like was correct: a spear, a rope, a wall, a tree, a snake, and a fan. Though they were right about their individual perspective, none had the entire picture.

Like the blind men and the elephant, the trick to effectively using big data is to get your organisation as a whole to contribute the understanding of your elephant and to coordinate efforts in order to use big data for identifying opportunities and acting on them.