While big data presents a range of potential benefits, there are also significant challenges that organizations must face in order to enjoy these advantages. In addition to the sheer amount of information necessary, firms must have the secure, expansive computing environment needed to house this data, as well as the tools and expertise to analyze it for key insights.
Overall, companies in the financial sector have their work cut out for them when it comes to their big data initiatives. Thankfully, the allure of utilizing these findings to boost customer service, establish a competitive edge and maintain a favorable position in the marketplace make overcoming the challenges of big data more than worth it.
A wealth of information ripe for analysis
Organizations in the financial sector have a variety of sources that they can leverage for big data analysis. According to a report from PricewaterhouseCoopers, 2.5 quintillion bytes of data are created on a daily basis. What's more, 90 percent of the big data that exists in the world today was created in the last two years.
This trend is also having quite the impact on the market associated with big data. PwC found that this sector is on track to reach a value of $53.4 billion by 2017, a considerable increase over 2015's $32.1 billion. Businesses in the financial industry especially have been increasing their use of big data strategies, expanding the marketplace for these insights.
As the trend toward big data continues, users across the globe will create more repositories of information that can be analyzed for insights, growing the potential benefits that big data analysis can offer.
"As we see it, the exponential advancement of social media, mobile and cloud in today's world — combined with relentlessly escalating regulatory pressures around the globe — is in turn pressuring financial institutions to rethink the way they do business," PwC stated. "Before they can succeed in gaining a competitive edge in today's dynamic, digital global marketplace, institutions need to evolve into data-centric organizations."
Understanding the challenge of big data
However, becoming a "data-centric" firm doesn't happen overnight. There are specific steps that banks, credit unions and other financial service providers must take to ensure that they understand the complexities involved with big data and can effectively overcome them.
A main issue here is the fact that big data builds upon the processes of traditional data management. For this reason, the practices that have been used in the past for data management simply cannot handle the requirements of big data.
Differentiating factor: The 3 Vs
The main difference here, according to PwC, is that big data can handle what is known as the three Vs — volume, variety and velocity — whereas traditional data management strategies fall short of this task.
TechTarget noted that the three Vs were first introduced by Gartner analyst Doug Laney in 2001. However, thanks to the insights this approach has provided, the practice is still heavily relied on today:
- Volume concerns the amount of information involved in the process.
- Variety denotes the different types of data, including the range of sources from which it is gathered.
- Velocity refers to the speed at which this data can be processed and analyzed.
PwC explained that the three Vs can be applied to both traditional data management as well as big data. However, big data practices were created with these elements in mind, and is able to leverage each V in a way that best benefits the organization gathering and analyzing the information.
"Focusing on large-scale data acquisition, big data requires little organization, has quick turnarounds for deep analysis and promotes innovation," PwC noted. "Traditional data management practices — such as warehousing, business intelligence and master data management — support business operations and focus on creating long-term consistency and trust in enterprise information. But the rise of big data need not mean the death of traditional data management practices."
In this way, financial organizations can leverage big data for more streamlined and immediately applicable insights. Traditional data management can then be utilized for more long-term, overarching business acumen.
Leveraging the three Vs for the best benefits
When viewed from the perspective of the three Vs, big data can offer a range of advantages, particularly for organizations in the financial service sector. However, there are a few best practices that should be put in place to ensure that these benefits are reaped in a tangible way. Big data, by nature, includes large repositories of information. Thankfully, solutions like colocation have made it easier to draw these details from a range of different sources and store them in a single location. This enables for analysis techniques to take place in a more organized and streamlined way.
"[I]nstitutions are recognizing that big data is the wave of the future, and that they need to gear up to prepare for the surge," PwC noted. "In our experience — and as research shows — investment in data management is rapidly increasing among financial service firms globally."
To find out more, contact Data Realty today. As partner firms specializing in the big data needs of financial services organizations, Data Realty and Aunalytics have all the solutions today's banks and credit unions need to support their analysis initiatives. From colocation space to hosted and managed Hadoop hardware clusters for analysis, Data Realty is a robust solution providers uniquely positioned to help financial service firms further their big data initiatives in a way that will best benefit their individual companies and the industry at large.