A CIO’s guide: How to get the most out of your data analytics investment
Statistical Data Analysis

A CIO’s guide: How to get the most out of your data analytics investment

Close to 80% of Chief Information Officers surveyed in the 2017 CIO 100 research said that they would be prioritizing spending on data analytics in 2018 and 2019. Given that 80% of the organizations surveyed in the 2018 The Future Belongs to Intelligent Operations by Accenture and HfS Research said that between half and 80% of their data is unstructured and largely inaccessible, this investment is hardly surprising. But how can companies ensure they are getting the most out of their data analytics investment?

The maxim data is the new oil implies that data is an untapped and valuable asset in the digital economy, promising huge rewards for those who tap into it. For Hunter Mcdonald, CEO at Tutela, there is a partial truth in this; data is increasingly central to the smooth functioning of companies the world over.

That said, Mcdonald adds that data’s latent value has not yet been truly unlocked and the reality is that data analytics is still very much in its infancy. “Using the oil analogy, people understand how to use the resource to generate value and also understand that there is a fixed quantity available. Thus far, data doesn’t have either of these characteristics,” he says.

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Getting to grips with the volume and type of data flowing through an organization can be daunting. The Global IoT Survey by Vanson Bourne, for HCL Technologies, found that organizations are struggling to use the large quantities of data that they are now collecting. As much as 91% are seeing challenges in turning data into insights. Obstacles include not using the right platform (46%) and platforms not being scalable (31%). Consequently, many organizations are collecting ‘dark data’, where answers are left untapped, which means that organizations are missing out on highly valuable insights.

On average, the survey found, it currently takes organizations nine days to convert data into useful insight, which is more than four times longer than they would like; two days is their average ideal time. The need to ensure that investment in data management and analytics yields values is more crucial than ever.

 

Why the changing role of CIO is important

David Murray, Chief Business Development Officer at Corvil, believes that getting data analytics investment right starts with understanding the evolving nature of the CIO role.

Historically, he says, the CIO role was seen as the custodian for technology infrastructure, and then the custodian for mechanics of applications and support of applications. The evolution into digital business and what will continue to evolve into active and artificial intelligence and automated business will see that role evolve even more.

“The CIO essentially is sitting on the data fabric for the business because at some point all data for the business is going to traverse or interact with an application or some aspect of infrastructure that the CIO or their organization has brought together,” Murray says.

Murray stresses that the CIO has to be able to contribute and add value to the business. Traditionally, he says this has taken place more so through large initiatives than on a continuous business as usual basis from the perspective of really impacting and driving day to day decision-making in a business.

The challenges are rooted in the fact that an awful lot of the data that flows through an organization is unstructured and inaccessible. “Most organizations were not built to run exclusively on data,” he says, “Amazon and Uber and the next generation were built on data, so they started from the beginning with the understanding that the value of the business was in the data that they had and the ability to then monetize and use that data for intelligence to create a better customer experience and to make every customer interaction more effective and successful. Most CIOs don’t have that luxury.”

Murray explains that most CIOs grew up in organizations whose technology evolved over 30 to 40 years or more. “You have all the problem of infrastructures that over time were more bolt-on. Being able to evolve beyond this is challenging. The same is true of data and data management. CIOs are at the heart of harnessing data and need to be able to make better use of data, both structured and unstructured,” he says.

For many organizations data analytics means investing in new systems, but that alone does not translate data into value. Laurie Miles, Director of Analytics for SAS UK & Ireland, says: “It’s not the size of your spend that matters; it’s what you do with it. CIOs need to make sure they know what they’re trying to achieve when they invest in data analytics, and have the right people and solutions in place to make it work in practice.”

SAS’ research with CEBR found that while 84% of businesses do experience an increase in revenue following an investment in big data, some industries see much larger growth than others. For example, investment banking saw an average boost of 12%, whereas retailers only reported a rise of 7%. “Investment in analytics isn’t enough if you can’t fully implement the insights you get from data,” she says.

“Don’t just analyze your data and then stop there – you need to ensure the insights are turned into actions that change the way you run your business. By using large-scale analytics to create actionable, real-time insights, you can improve the efficiency and performance in many areas of your core business – whether that’s better customer experience, supply chain optimization, fraud detection or anything else.”

Miles adds that simply reducing expenditure on unnecessary or inefficient processes can allow you to plough those savings back into your best performing activities, boosting revenue and profit as a result. But this will often require the organization to become more data driven as a whole, with more data and insight sharing across different departments.

Murray believes that part of the investment is building a foundation, a more data-oriented mind-set. This means taking the human capital that you have and building a mind-set around the value of data. “You need to think from a perspective of every piece of data is important,” he says.

 

Why business considerations must be front and center of analytics

Investment in human talent is as important as technological infrastructure. “If we fast forward 15 years, the people who are going to be most influential in AI are going to be people building those AI models and that’s where you are going to see the best of the best. Talent that can grow with advanced expertise in this area is incredibly important,” Murray says. “Invest very heavily in top human capital in this particular area.”

From the technologies perspective, Murray advises that CIOs need to invest in technologies that allow them to obtain data from untraditional sources. Corvil, for example, collects information as it is being communicated across the network and are able to create value and intelligence from that.

For Doug Bordonaro, Chief Data Evangelist at ThoughtSpot, the biggest mistake IT organizations make is driving analytics programs from the data side instead of the business side. Bordonaro advises that instead of trying to make all unstructured data accessible through analytics tools for some nebulous purpose, it is far more effective to identify a targeted business problem and focus on making the data that can solve it available to those in the business who can act on it.

“This will deliver fast, early success to help drive the momentum of the overall program, and also target valuable, useful data instead of the noise that’s always inherent in unstructured data,” he says.

Murray agrees that, at the strategic level, it is important to think in terms of specific, more structured outcomes. “Sometimes it is about how you solve the problem or how you structure the outcome, asking the right question rather than finding the right answer,” Murray says.

CIOs have long been plagued by large efforts that have ultimately failed. “We see big data projects that fail all the time. Start by looking to derive specific outcomes and building a solution to answer those questions and then building on it from there. The concept of ‘collecting everything and I will figure out what to do with it later’ has not proven to be successful for large organizations,” Murray warns.

“While all data can be valuable at some point, you have to start with the data you need to answer the most important questions and prioritize accordingly.”

Murray adds that businesses should want to create an open environment for departments and for people to answer their own questions so while they provide an aspect of a shared services model, they should also be able to extend out and empower teams to use data that is most important to them to answer questions. 

 

What it takes to drive success in data analytics initiatives

Alexey Utkin, Principal Solution Consultant: Finance Practice at DataArt, believes there are three components of success for data analytics initiatives. First, Utkin says, you need access to quality data to analyze. “It sounds obvious, but availability and quality of the data are the biggest hurdles for most companies aspiring to drive their business forward with data analytics,” he says.

Secondly, the rapidly evolving area of data analysis itself is new for many companies, and it requires new skills, approaches, and technologies. “Widely used open source and cloud data analytics technologies are powerful options to consider here,” Utkin advises. Lastly, often an overlooked aspect, is an established roll-out approach of data insights to business decisions, processes, systems, and operations, which over time will evolve and create data-driven business culture.

Shawn Rogers, Senior Director Analytic Strategy at TIBCO Software, adds that CIOs also need to understand that demand has evolved and the company has a growing and diverse community to serve with analytics.

Early on, analytics were consumed by a small portion of the organization, but today there is greater demand from a growing population that wants to infuse their role/ department/ business with smarter insights and action. “Architecting systems to serve line of business executives, analysts, data scientists, customers and partners, is a powerful way for CIOs to drive return on investment,” he says.

Rogers warns that the last mile of data for many companies can require a significant investment. Accessibility and trust are critical, but are lessons missed by many companies. “Spending a million dollars to collect unstructured data in Hadoop can be exciting but for many companies the investment can turn into a ‘Hadump’,” he says, adding that making data easy for your community of analytic consumers is core to success; it will build trust and also lead to adoption. “Data virtualization, catalogue technology, Master Data Management (MDM) and data wrangling at the application level, are all components of leveraging data and enabling BI/analytic adoption.”

For Kaushal Mody, Global Delivery Excellence and Innovation Director for Accenture Operations, anchoring of ‘data’ goes beyond analytics and business intelligence. Intelligent Operations calls for what Mody calls a diverse data-driven backbone that harnesses the data explosion and powers technologies of automation, artificial intelligence and advanced analytics to drive superior outcomes across every layer of business operations. “We’re telling clients to have a holistic view beyond analytics and business intelligence,” he says. 

He explains that in enterprise functions, siloed data has been the status quo for many organizations. However, now organizations need to aggregate and harness diverse data from their end-to-end processes, their suppliers, customers and also from peers and industry to keep pace with the need for insights and for decision-making. In customer functions – besides the obvious sales, order, fulfilment and financial data and visible unstructured social data – a wealth of diverse data exists in customer interaction logs, service platform notes, and voice recordings to enable insights and personalized experiences.

“Current strategies, that enterprises are designing and implementing, include data aggregation, data lakes, or data curation, as well as mechanisms to turn data into insights and then actions,” Mody says.

Bordonaro says that unstructured data is like raw ore. “There’s probably valuable material in there, but there’s also a lot of chaff.”

Dave McCarthy, Bsquare’s Senior Director of Products, cautions that simply visualizing complex data does not, by itself, yield better business outcomes, and sophisticated and advanced forms of data analysis should be used. For example, dashboards might seem critical to IoT data analysis, but they actually suffer from two fundamental problems: scale and pattern recognition. “Humans simply cannot keep up with the volume and scale of data being generated – data from thousands of devices being delivered at a high frequency – no matter the visualization technique,” he says.

Miles emphasizes that it is essential to invest in training and hiring data scientists to help you extract what you need from all your data. “Investment in technology gives you the tools, but you also need people who understand the business problem, how to use the tools to tackle that problem, and then how to explain the results back to the business. Whether that’s a dedicated in-house team or an experienced partner, human capital should always be central to your investment decision,” she says.

Mcdonald adds that the skills field is skewed and there is a lack of skilled practitioners. “Because data analytics is still maturing there is a corresponding lack of expertise required to manage and make sense of it. The skills do exist but they tend to get gobbled up by data giants like Google who do all they can to attract this type of talent. As such the skills field is slightly uneven and therefore organizations need to work hard to attract talent.”

Balancing the technological and human capital components of data analytics and management will remain a key challenge for CIOs as data continues to power modern enterprises and companies learn to tap into the raw ore that is data.

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Bianca Wright

Bianca Wright is a UK-based freelance business and technology writer, who has written for publications in the UK, the US, Australia and South Africa. She holds an MPhil in science and technology journalism and a DPhil in Media Studies.

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