The Data Driven Revolution
In 2010, as the world economy began its rebound from a debilitating financial crisis, The Economist published a seminal piece on the importance of data in our lives. Sporting an illustration of a businessman with an upside-down umbrella, attempting to collect binary numbers as they fell, the author coined the term “data deluge.” As The Economist ended the piece, it wisely warned us that “the process of learning to cope with the data deluge, and working out how best to tap it, has only just begun.”
More than four years later, it is hard to argue with the validity of this statement. Data is being generated at a pace that was unimaginable a decade ago, and organizations are still struggling to beef up their infrastructure and statistical prowess to prepare for this new era.
According to the IDC, big data spending is expected to increase by 30% this year, surpassing $14 billion. Rob Bearden, the CEO of Hortonworks, echoed the importance of enterprise data at his 2014 Hadoop Summit keynote speech when he stated that "the data volume in the enterprise is going to grow 50x year-over-year between now and 2020...the most important thing to recognize is that 85% of that data is coming from net-new data sources."
Figure 1: Timeline of Key Data Analytics Milestones
Despite the potential of big data, managing the massive amounts of data generated by customers and enterprises can be overwhelming. CMOs are constantly hearing about how they must use data to evaluate their marketing campaigns, operations managers are well aware that the use of data can optimize their supply chain, and finance executives are clamoring for ways to use analytics to realize cost savings. However, many organizations don’t know where to start or are stuck at an unsatisfactory halfway point.
I believe that by harnessing, analyzing, and distributing data pragmatically and consistently, “data deluge” can be avoided. Most importantly, data driven decision making can be turned into a competitive advantage, which will separate those who lead from those who fall behind in the next several decades.
There is no shortage of thought leadership discussing the importance of data driven business insights. However, this thinking falls in two camps:
- Discussion of the business impact of data (e.g. improved CAPEX, human capital improvements, marketing effectiveness) without focusing on the technology or computing elements.
- Focusing only on the technological or computing aspects of analytics (e.g. in-memory analytics, data platforms) without highlighting the business impact.
In my writing, I hope to effectively blend these two worlds together.
Figure 2: Intersection of Analytical Business and Computing
While analytics is important in each functional area and industry, I would like to focus on the following areas throughout the course of my writing.
- Customer Insight: Customers are more connected, demanding, and technology savvy than they have ever been before. All organizations must create personal and authentic experiences to deepen customer relationships and customer share of wallet. I plan to write about key customer analytics trends and best practices to help organizations capitalize on this fast growing field.
- Self Service Analytics: As personal technology has advanced, enterprise technology users have become accustomed to making decisions in a more collaborative and mobile way. Self-service analytics, or the ability to analyze data without the use of IT, has become an important way to empower business users. I plan to write about technological platforms that enable self service and how companies can implement successful self service platforms.
- Emerging Markets: The top ten growth markets for technology devices in 2015 will be emerging market economies. These markets are extremely attractive to multinational companies due to their rising middle classes and the increasing number of educated, English speaking, and technologically savvy young people in their countries. However, it is challenging to use data to understand these customers. Data collections agencies in emerging markets can be inconsistent in quality and their data limited. I plan to write about how organizations can integrate high-quality public data through open-source analytics platforms to obtain deeper customer insights.
- Transitioning from BI to Predictive Analytics: As the price of computing power has decreased and the power of such systems have increased, the barriers of entry for predictive analytics have dropped. However, organizations still struggle with the transition from business intelligence (BI) to predictive analytics. Fierce competition for data science talent, a misunderstanding of predictive technologies, and challenges operationalizing analytics have kept organizations mired in the awkward middle ground between BI and predictive analytics. I plan to write about how companies can successfully complete this transition.
I am excited to share my thoughts with you and hear your comments on what I have written. We are in the midst of a tremendously exciting time for enterprise technology. The data driven revolution has only just begun - I am thrilled to be part of the journey.
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