Building the Business Case for Big Data Analytics

The amount of data being transported, stored, and processed is growing exponentially. Raw data has very limited value, but when it is analyzed and turned into information, it becomes valuable. Digital service providers, such as Amazon and Google, are leading the way in understanding how to make raw data valuable, and many others are playing catch-up. New tools and techniques are being developed all the time to make this task easier.

Data analytics provides significant insight into customer satisfaction so that customer needs and behaviors can be better understood, and new sources of revenue can be identified. It improves customer satisfaction by identifying where customers are experiencing problems with a service or website; it helps prevent fraud and minimizes risk; and it offers the potential of monetizing from third parties.

ZTE is well-known for providing multiple solutions and is vocal in making the business case for big-data analysis. ZTE understands the concept of big data and provides support for

●    identifying the particular business case for big-data analysis

●    estimating the start-up investment

●    creating a data-centric culture and determining  how it will affect people, processes and systems

●    identifying the use cases for data analytics and where it will most add value

●    commodifying the data, i.e. aggregating, packing and selling customer insight

When communication service providers (CSPs) think of big data, they first think about the network and the challenge of managing it. However, big data isn’t just about CSP network management; it is an opportunity for an operator to redefine how they interact with customers and partners and how they interact internally. This means that we need to redefine big data.

What is Big Data?

People have many different opinions about big data, and it has been defined in many different ways. However, most agree that it has three main characteristics: volume, velocity, and variety. A recently published Gartner report offers the following definition: Big data is high-volume, high-velocity, and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. The veracity of the data and the value of customers as the source of the data are also of utmost importance to CSPs.

CSPs have been managing, safeguarding, and storing their big data. However, the opportunities lie in putting this data in the hands of people who can analyze it in order to make better-informed decisions. Days-old data summarized and stored in a warehouse may be useful for managed reporting of routine information, but it is not suitable for fast-changing, unanticipated management information needs.

The new paradigm in analytics infrastructure is real-time visual discovery of large amounts of data. This allows a business to find critical information in a timely manner.

Business Case and Initial Investment

There is no singular method for providing a business intelligence solution to meet a company’s unique needs. However, there is an approach to taking advantage of big data that minimizes risk and increases the likelihood of success:

●    begin with stakeholders. Categorize stakeholders by role, prioritize them in terms of information-making value and follow a step-by-step road map.

●    consider culture. Good decision making requires a cultural shift towards data-driven, fact-based decisions (as opposed to unsupported or intuitive decisions). Business leaders need to emphasize quantitative approaches to optimizing business performance.

●    find data stewards. Finding the right people to define data governance and implement data management processes can be tough. Complex analytics have traditionally been relegated to statisticians, analysts, data scientists, or other highly cerebral people. However, such roles are not part of the organizational charts of most companies, and integrating these roles with business management roles to solve problems can be a challenge. A new breed of data steward has a mix of technical and business skills and may be a single person or a member of a tightly aligned team.

●    set clear goals. Big data projects are difficult, so don’t try to boil the ocean. Instead, start small, show a win, and grow incrementally. Decisions informed by big data should be catalogued as use cases. The impact of each decision should be weighted, and goals should be drive by these use cases. The entire information management landscape should be scoped, but only pick the low-hanging fruit. Goals aren’t achievable unless they are smart, measurable, actionable, realistic, and time-bound (SMART).

●    create a plan. When developing a plan, the goals need to be linked to volume, velocity and variety. Big data is a complements (not replaces) existing analytics, such as data warehouses, OLAP, and decision support systems (DSS). Of course, no plan is complete without ROI projection, but don’t try to create an overarching “big data ROI” forecast. Instead, develop ROI forecasts by each use case.

●    establish metrics. There are many criteria for assessing the impact of a decision. These might include reduced risk, increased confidence, or quality of decision. However, I prefer to assess the impact of a decision in terms of financial metrics, either in the form of cost avoidance or incremental revenue.

●    deploy technology. By definition, big data is information that cannot be leveraged using traditional processes and tools, but it has the potential to resolve many common challenges. A technological starting point is the open-source big-data engine called Hadoop. This is particularly well suited for loosely structured or unstructured data as well as high-volume search and discovery.

●    make big data small. This means delivering small data in context (with business use cases) to decision-makers. In this way, insights are easily consumed and actionable. This is the last mile in making big data useful.

●    design for continuous process improvement (CPI). Making better business decisions is not a onetime activity. Incorporating a CPI methodology into a plan allows for learning, improved performance, and increased ROI over time.

Big data is often considered as a sea, which means there is unlimited data and thus there can be a lot of satisfactory factors in order to manipulate this data. If the satisfactory factors are given in hands of end users, big data has a never ending scope and more healthy investment is required. If big data organizations keep themselves on the driving seat to guide vend users, the investment becomes much smaller and simpler with minimum risks.

Use Cases for Data Analytics

Sometimes use cases can drive points home. In reality, big-data use cases are as varied as big data itself. Big-data analytics provide new ways for business and governments to discover new business facts that no one knew before. Big-data analytics has been used for

●    determining customer experience, including customer satisfaction and 360 degree customer view

●    predictive analysis

●    marketing according to customer behavior

●    electronic sales management, including all the factor of social media

●    finance

●    fraud detection

●    analyzing product usage

●    inventory analysis

●    customer service management.

Big Data Could Become Big Business for Telcos: Aggregating, Packing and Selling Customer Insight

Mobile operators know a lot about their customers. They know which cell towers a person is connected to and when they are connected to it. Therefore, they know where you are generally and can guess where you live and work. They also know which websites you visit on all your mobile devices. For example, an operator might see that you visited the Best Buy website on Monday and then connected to a cell tower near a Best Buy store on Tuesday. The operator could conclude that you did research online before going to a physical store to make a purchase.

Mobile operators have access to an enormous amount of data, but right now, they’re not putting it to much use. That won’t last much longer.

Telefónica launched Dynamic Insights late last year. This is a project to aggregate data, make it anonymous, and sell it to advertisers. A video on Telefónica’s web site says the group offers information about “actual consumer behavior instead of perceived behavior.”

Project Oscar is a joint venture between O2, Vodafone, and EE UK. Late last year, it launched Weve, which took an aggregated approach to users and data.

This is a whole new revenue stream for operators, and in the UK at least, it seems to be a higher priority than mobile payments. It is also something that operators are willing to cooperate on (unlike NFC payments).

Before getting too concerned about privacy and a dystopian future of Big Brother selling us Soylent Green, let’s consider the sheer volume of data we’re talking about. It’s really big data. Think about the billions of people in the world with mobile phones and the fact that more of us are getting every day. Consider the number of text messages we send in aggregate. (Last year, it was nearly eight trillion.) Consider the number of cell towers each of us passes by each day. Every couple of minutes, there’s a new location record for every phone in the world. Add to that calls, browsing, etc. and that is big, big data.

Big Data Risks and Challenges: Impact on People, Processes and Systems

Like all disruptive technologies, big data isn’t without its risks. A recurring problem is that companies put the technology ahead of processes, people, and specific outcomes. They work forward from the technology instead of backwards from business outcomes. While many understand the value of information harnessed from a myriad of internal and external sources, fewer understand how to make that information accessible and actionable at the exact point where it can be used by knowledge workers across the organization. The challenges to leveraging big data for SMART business objectives are no different than other information-analysis methods and must start with a plan integrating people, processes, and technology. Such a plan must include the processes for identifying and capturing the data, and the tools to manage (access, sync, merge, store, tag, and annotate) the data. It must also include the processes for the timely distribution of the data to the area where it can be applied for specific purposes and to achieve consistent results.

Another challenge with big data is relevancy. More data creates more noise. Business analysts need to classify data into a spectrum that ranges from noise to signals and base this spectrum, in large part, on the use case and weighted results of the data. Other challenges, such as data privacy, information security, information distribution, data presentation and even data overload, are not unique to big data, and risks and solutions can be learned from other business analytics solutions.

Conclusion

We have entered the era of big data, which is beginning to take center stage. Given this explosion of data, there is a dire need to glean useful business insights from it. Big-data analytics provides a way of gaining wisdom from otherwise useless data. Big-data analytics will be mission-critical in the enterprises of the future. There are some common challenges across a wide range of big-data applications, so it is not cost-effective to address the problems in one application alone. These challenges will require transformative solutions and will not be addressed naturally by the next generation of industrial products. We must support and encourage fundamental research on these technical challenges if we are to achieve the promised benefits of big data.