Transferring DATA into Business Value for Multi Sectoral Industries | CeDAWI – Applied Research Center for Data Analytics and Web Insights

Transferring DATA into Business Value for Multi Sectoral Industries

Abzetdin Adamov - Data Analytics and Web Insights

Assoc.Prof., Abzetdin Adamov,
Founding Director,
Applied Research Center for Data Analytics and Web Insights (CeDAWI)

Data Mining and Analytics can provide valuable insights and high growth potential to any data-driven business. According to survey performed by Bain & Company of more than 400 executives in diverse range of businesses and industries revealed that companies with better analytics capabilities were twice as likely to be in the top of financial performance in their industry, five times more likely to make decisions faster than their peers and three times more likely to execute decisions as planned.

Even if most of executives understand the opportunities provided to their businesses by Big Data, they still struggle with some common challenges:

How to align Big Data benefits to specific sector?
How to identify what kind of Data may provide best insights and improve decision making process?
Which specific technologies and skills are needed to understand their Data?

It is obvious that any sector can capture value from Big Data, but it is also important to keep in mind that approaches, skills, technologies and correspondingly opportunities and challenges vary from sector to sector.

RETAILING INDUSTRY

Big Data impact on RETAILING INDUSTRY

Retailers produce huge amounts of transactional Data storing purchase activities of consumers. Walmart processes over 1 million customer transactions, thus generating data in excess of 2.5 PB per hour. Analytics applied to this data can provide sophisticated insight into the timing and nature of purchasing decisions. Retailers can use data from variety of sources including POS records, social media, transaction processing, etc. to learn and predict customer behavior, optimize pricing, optimize stock usage, develop well tunned loyalty programs, monitor real-time processes.
One of the best examples of implementing Big Data Analytics in retail sector is Target’s experience skyrocketed which sales, and the company’s revenues have grown by $23 billion since the new data-driven strategy was implemented.

BANKING INDUSTRY

Big Data impact on BANKING INDUSTRY

Because of specific business model and wide use of cashless payments, Banking sector generates tremendous amounts of transactional data on a daily basis. This data clearly reveal the secrets of money movements, enable fraud prevention, help to understand consumer payment behavior. Now banks can quickly process huge volumes of data including historical data that been storing since decades. Using advanced Data Analytics they can extract valuable information from legacy data and convert it into meaningful benefits for themselves and their customers. Big Data Analytics will be the critical game changer for the Banking Industry and part of their core strategy.

TELECOMMUNICATION INDUSTRY

Big Data impact on TELECOMMUNICATION INDUSTRY

As one of the most truly Data-driven business sector, telecom companies ask crucial question: How can they benefit from Big Data. Unlike to classic approach which firstly determines business problems and after looks for appropriate data that can help to solve these problems, Telecom companies have to start with own data experimenting on it, searching correlations between attributes, discovering most frequent patterns and transforming all of these into actionable insights. It is very well known fact that attracting new customers it is more expensive than retaining of existing ones. This is especially true for Telecom industry, where detecting the “going to be churner” customers beforehand is one of the most critical challenges. The solution is a Telecom Data Analytics system that can detect churn risk and help to develop customized campaigns for the customers who are at high risk. Besides Churn Prediction the Data Mining and Analysis in Telecom industy can be used also for Insolvency Prediction, Fraud Detection, etc.

HEALTHCARE INDUSTRY

Big Data impact on HEALTHCARE INDUSTRY

Modern healthcare sector is one of the most Data-driven industries. Simultaneously producing huge amount of medical Data like doctor’s prescriptions, clinical records, lab results, X-ray and MRI images, biomedical/genetic data, etc. And it looks like the future holds even more sources of data, like patient-generated tracking from wearing devices like blood pressure and body temperature sensors, odometers, etc. Healthcare data is diverse and complex making linear analysis, which works well for sectors, useless here. Using right approaches and tools applied to the right Data can predict epidemics, cure disease, improve quality of life, determine warning signs of serious illness at an early enough stage.

ENERGY (OIL and GAS) INDUSTRY

Big Data impact on ENERGY (OIL and GAS) INDUSTRY

Oil and Gas industry is traditionally dealing with large scale data. In order determine natural resources laying underground and bring them out, it is essential to retrieve and analyze seismic data. Today, increasing computing power and lower storage costs in conjunction with advanced data mining and analytical technologies (3V of Big Data – Volume, Velocity, Variety) help oil producers to develop adaptive drilling strategy and increase efficiency of production. Analysis of Data from variety of sources including weather, soil, equipment (drill heads, hole sensors, etc.), seismic and geological data highly effects the success of drilling operations and has direct impact on cost and revenue.

MARKETING INDUSTRY

Big Data impact on MARKETING INDUSTRY

Thanks to pervasive availability of data and right technology to analyze this data, marketers have the opportunity to understand customers in depth having deep, relevant and constantly improving dialogue with customers. This new marketing approach that potentially applicable at the Internet created new concept of “Big Marketing”.
More and more people are going online on daily base dealing actively with Digital Channels. There are variety of Digital Channels:

Internet (websites, searches, ads),
Social (publish, shares, likes, comments),
Mobile (location, apps),
Interactive (OOH out-of-home)

Traces produced while potential consumer deals with Digital Channels can help to turn this data into Consumer’s Digital Signature. For example, online retailers may have only a few seconds to attract customer’s attention on their website before they move to another web-page. So, using Digital Signature of customer and applying right patterns marketers can predict purchase behavior and decide the best offer to make to individual customer.