The term big data analytics is rooted in a trend that began to emerge in the 1970s with the development of the world’s first commercial data centers. As digital technologies enabled greater data collection, so did the need to collect and organize this data. Over the intervening two decades, data collection went from a trickle to a deluge. Today, smart devices like mobile phones and computers collect thousands of data points per user per day. Collectively and when connected to the internet, these devices result in billions of data points generated each day across the internet. As a result, big data has come to denote a competitive advantage that every business is gunning for.
But the promise of big data has been difficult to actualize. The major challenge businesses face when it comes to big data is deriving actionable insights that translate into increased profits. For many businesses, the concept of big data is still in the realm of fantastic innovations. Nonetheless, more pragmatic applications of big data are possible today thanks to advancements in data collection and storage technologies like Hadoop and Spark from Apache, as well as burgeoning Silicon Valley startups like Arcadia Data and Drawbridge that use AI-enabled big data analytics to generate on-demand actionable insights for businesses.
For business leaders questioning where to start their companies on the journey to becoming big-data-enabled organizations we have identified four priority areas to focus on:
- Identify critical patterns
- Increase customer-focused productivity
- Identify and reward employee efforts
- Accelerate the development of breakthrough solutions
Identify Critical Patterns
Netflix and P&G have one thing in common; they have transformed themselves into data companies that happen to sell entertainment and consumer goods respectively. Both companies rely heavily on consumer trends to create and curate goods and services for the market. Without some way of understanding these trends and anticipating future needs, both companies would make costly mistakes. By harnessing big data, which is uniquely equipped for pattern recognition, both companies can extract critical market patterns and respond accordingly.
Zooming in to smaller organizations, the same capabilities can be harnessed albeit at a smaller scale. To get started, organizations must first identify and tag their sources of data. This could be clickstreams, social media, mobile apps, or other sources. Next, organizations must process this (often unstructured) data into structured data. This can be accomplished either in-house using off-the-shelf tools, or by partnering with big data analytics companies. From this data, patterns identified may include spikes in traffic at certain times of the year, app usage at certain times of day, per-customer-segment purchasing trends, and others. Such insights may point to either operational inefficiencies or new market opportunities for exploitation.
Increase Customer-focused Productivity
Customer-centricity is a growing trend among leading global companies. Driven by the rise in social media and the resultant brand transparency this has forced on companies, today, customer centricity is as much an essential business strategy as any other. But making that transition is often fraught with corporate culture challenges, especially for large legacy businesses that have been focused inwards for decades. This shift can be made easier through big data.
Amazon is well-known as a customer-centric company. What drives this culture is not just goodwill, but technology. The company is maniacal about optimizing everything through technology. By using data collected from millions of purchases, returns, customer care inquiries, and other data points, the company has managed to implement customer-focused processes that have clearly identifiable outcomes.
For instance, it is conceivable that processing returns quickly translates into customers resuming purchasing faster. This could be the main reason the company has fine-tuned return processing at a customer care level, which points to customer-focused productivity, as data shows this is a path to ensuring uninterrupted purchasing flows.
Organizations seeking similar capabilities must optimize their data mining and analytics efforts to identify areas where greater customer-focused productivity can be achieved.
Identify and Reward Employee Efforts
It is well-known that happy employees make happy customers. This is supported by a Gallup poll that found that increased employee engagement resulted in improved customer relationships and a corresponding 20% jump in sales. One of the major challenges with achieving this happy employee-happy customer matrix is identifying employee efforts and rewarding them accordingly. This is something traditional scorecards, performance reviews, and other KPI-measuring tools cannot effectively do.
Big data, on the other hand, is inherently capable of accomplishing this. For instance, by transcribing support calls and performing sentiment analysis, it is possible to isolate and reward support agents who went above and beyond their mandate when providing support. Also, processing internal corporate communications can help identify gaps in collaboration, information and skill silos and what actions employees can take to address these issues. Such actions can similarly be identified and rewarded as a means of enforcement.
Accelerate Development of Breakthrough Solutions
Creating new drugs is often a 10-15-year cycle that involves hundreds of lab experiments, most with a 90% failure rate. IBM is using big data to slash this cycle timeframe. At its Almaden research center in San Jose, California, the company is pulling in data from diverse sources including patent applications, university publications, science journals among others and processing this data using Watson Ai to find combinations of data that can offer insights to accelerate development of breakthrough solutions.
Companies can similarly accelerate their own discovery efforts by analyzing their data either to find improvement opportunities to existing solutions or discover novel ideas for new products and services. The discovery process must, however, be guided by business-level governance models that focus on innovations that either strengthen or build on existing core competencies. Partnering with Silicon Valley startups may be a viable option, especially if the company wants to build upon already proven technologies and approaches.
Big Data, Big Opportunity
Data is emerging as the ultimate competitive advantage as it gives companies near-seer capabilities to understand and anticipate customer needs. For companies keen on experimenting with big data, it is crucial to implement a roadmap to achieve the capabilities outlined above based on the following key structures:
- Align big data efforts with business objectives
- Plan for skill optimization to support big data push
- Create company-wide big data standard operating models
- Align acquired (unstructured) data with internal (structured) data to create a single-view
- Earmark key deliverables from efforts, even if such deliverables include failed experiments
Visit Silicon Valley Big Data Startups
Silicon Valley Innovation Center helps financial sector executives experience and connect with the Silicon Valley fintech startup ecosystem through the Leading Digital Transformation executive immersion program. As Silicon Valley is a hotbed of big data innovation, company executives benefit greatly from visiting the innovation hub and interacting with startups like the ones mentioned in this article. Through this immersive experience, executives also gain deep insights into how partnering with Silicon Valley startups can be a game-changer for their businesses.
