Addressing the 'Human Element' in Data Analytics
What are the current market trends you see shaping the data analytics space? What is your take on incorporating those trends to make them effective through your solutions?
EY recently launched a joint survey with Forbes Insights on this very topic. Some of the key findings revealed that leading organizations that use advanced analytics are seeing double digit growth of above 15 percent in revenues and operating margins, as well as improved risk profiles. Furthermore, half of the global survey participants plan to allocate at least $10 Mn over the next two years.
The survey also revealed that fundamental problems arise at the crucial linkages, synapses, between the steps organizations take as they move from identifying new business opportunities, acting on insights and then measuring the outcomes. These synapses connect key steps in the analytics lifecycle: Competitive differentiation, operating model, initiative design, intervention design, and measurement & learning.
From a functional department standpoint, the findings also revealed
• Customer service caught-up with IT for the #1 spot
• Customer service and sales made the largest improvement year-over-year
• Human resources showing the biggest jump, driven by intense global competition for talent
• Strategy and innovation behind all departments
The data in the survey is pretty compelling and gets to analytics being an enterprise capability that can add value across the organization. This also highlights the need to have a proper organizational model and management of a portfolio of initiatives. Our solutions are meant to help organizations really accelerate and scale the value that can be created across the organization.
What are the common businesses challenges organizations providing data analytics services face at this point in time? As a technology enthusiast, please opine your views on the steps organizations should take in combating those.
The ‘human element’ continues to be a common pain-point in realizing value and driving transformational change. Culture, collaboration, and skills were cited as the biggest challenges throughout the analytics lifecycle. We define the human element as: culture and leadership; organizational and process design; learning and development; skills and incentives. There needs to be better collaboration among IT, data analytics and business teams.
Analytics should be treated as an enterprise-wide strategy, not an ad-hoc endeavor that varies from department to department
Here are a few recommendations to address this:
Ensure advanced analytics initiatives are closely aligned with the overall business strategy and how the organization creates competitive differentiation. As more data is unified and created across the enterprise, leadership has the opportunity to ask better questions and leverage an asset that their competitors do not possess–insights about their operations and customers.
Consider what new products, services, and capabilities can be created by considering data as an asset in its own right. The value of data as an enterprise asset, and the insights derived from advanced analytics, comes as data is de-siloed and the enterprise embraces experimentation to drive real innovation. The continued adoption of big data technologies, cloud services and machine leaning or AI have provided an opportunity to experiment at scale, cost effectively.
Focus on creating alignment and closer collaboration among stakeholders from all relevant departments–define what ‘good’ will need to look like and remove organizational and policy barriers to effectively execute.
Rapidly changing workforce demands and the demand for on-site technology silver bullets have pushed technology executives towards performing a balancing act. What are your views on how these can be timed to execution?
• We are witnessing more traditional process-driven organizations being disrupted by the new wave of businesses that are using data as a strategic asset at an enterprise level to rethink and reimagine their entire business.
• This has created a disconnect between business and IT executives. For example, while 71 percent of CIOs or CTOs and 67percent of CEOs or Presidents or COOs believe there is a high level of effectiveness among business users and technical people, department managers aren’t nearly as upbeat. Just 46percent of chief financial officers, 43 percent of chief analytics officers and 37 percent of chief risk officers agree with that assessment.
• To better utilize data for strategic gain, enterprises already using advanced analytics see the need to foster a cultural shift designed to promote collaboration and data analytics skills.
• When driving innovation and ultimately growth, global executives agree on one thing—analytics should be treated as an enterprise-wide strategy, not an ad-hoc endeavor that varies from department to department.
• Critical to address this are a couple of key steps which include:
• Building bridges between the business and technical teams to design a solution that you want to put in place.
• Find common ground to build synergies and more importantly, move forward.
• Being proactive in expanding your network of trusted relationships with senior stakeholders throughout the company. Focus on building those relationships and working with other leaders in the organization to boost productivity. This is a critical skillset that organizations need to pay more attention to.
Nowadays, a lot of hype is forming around data analytics and both growing players and big-fishes in the market are ideating its benefits. What are the advantages of using data analytics for an enterprise?
• Of the executives we surveyed who have an analytics strategy that is well-established and central to the overall business strategy, 66 percent achieved revenue growth of 15 percent or more, while 63 percent reported that operating margins had increased 15 percent or more in 2016.
• We also see organizations showing a mix of tactical and strategic goals, with a desire to develop new products or services also on their analytics wish list.
• The potential to employ data and advanced analytics strategically takes on even greater importance for the leaders—they certainly see tactical opportunities, but they’re even more interested in how they can use data to strengthen themselves in the future. They want to transform business models, develop new products, react more quickly to market changes, and develop closer relationships with partners and vendors.
What is your take on ensuring data availability?
• To create a culture that encourages innovation, organizations must look to break down barriers and open the flow of information throughout an entire organization.
• The potential to employ data and advanced analytics strategically takes on even greater importance for the leaders—they certainly see tactical opportunities, but they’re even more interested in how they can use data to strengthen themselves in the future.
• They want to transform business models, develop new products, react more quickly to market changes, and develop closer relationships with partners and vendors.
• As less advanced companies evolve their data and advanced analytics strategies, they must embrace a similarly forward-looking orientation as typified by the leaders.
What is your advice for budding technologists in the data analytics space? How do you see the evolution few years from now with regards to disruptions and transformations within data analytics infrastructure field services?
• As the Chief Analytics Officer for EY and in speaking with many leaders in different industries, I’m finding analytics to no longer be just a technology issue, but a strategy and operational issue. Big data and analytics is a disruptive innovation that is transforming our everyday lives.
• Enterprises need a holistic plan for creating competitive advantage, identifying opportunities and ultimately measuring value. We believe that there is tremendous value to be gained by those organizations that leverage analytics to transform their business processes and how they make decisions. As a result, we are seeing the emergence of the chief analytics officer as more of a senior-level change agent, with a unique combination of skills in business, mathematics and technology – to work with clients to identify new opportunities.
• In addition, we are seeing that this is a priority area for investment. 85 percent of global executives are expecting to invest in data and advanced analytics, with more than half planning to allocate at least US$10Mn each over the next two years.
• In the area of emerging technologies, market leading organizations use predictive modeling (67 percent), artificial intelligence (53 percent), and robotic process automation (43 percent).
• Companies need to integrate technologies together like AI, RPA, Blockchain, etc. Start with the business issue and then work into how various technologies come together to be a part of an integrated and differentiated solution.
Getting the Most out of Big Data
Big Data: Separating the Hype from Reality in Corporate Culture
Maintaining Maximum Relevancy for Buyers and Sellers
Building Levies to Manage Data Flood
By Tom Farrah, CIO & SVP, Dr Pepper Snapple Group
By George Evans, CIO, Singing River Health System
By John Kamin, EVP and CIO, Old National Bancorp
By Phil Jordan, CIO, Telefonica
By Elliot Garbus, VP-IoT Solutions Group & GM-Automotive...
By Dennis Hodges, CIO, Inteva Products
By Bill Krivoshik, SVP & CIO, Time Warner Inc.
By Gregory Morrison, SVP & CIO, Cox Enterprises
By Alberto Ruocco, CIO, American Electric Power
By Sam Lamonica, CIO & VP Information Systems, Rosendin...
By Sven Gerjets, SVP-IT, DIRECTV
By Marie Blake, EVP & CCO, BankUnited
By Lowell Gilvin, Chief Process Officer, Jabil
By Walter Carvalho, VP & Corporate CIO, Carnival Corporation
By Mary Alice Annecharico, SVP & CIO, Henry Ford Health System
By Bernd Schlotter, President of Services, Unify
By Bob Fecteau, CIO, SAIC
By Jason Alan Snyder, CTO, Momentum Worldwide
By Jim Whitehurst, CEO, Red Hat
By Marc Jones, Distinguished Engineer, IBM Cloud Infrastructure