Data analytics is continuing its inexorable rise as a driver of business strategy, boosted by the uptake of big data, artificial intelligence and machine learning, but it is still being hampered by an inability to change company culture.
That is the conclusion of the latest report by NewVantage Partners – now in its sixth year – whose authors claim this year’s results are both more encouraging and more worrying than in the past.
Six years ago, the primary focus of questions and answers in the survey was big data, which at that time was a relatively new phenomenon. In the 2018 survey, the primary attention has moved to AI. There is both a stronger feeling that big data and AI projects deliver value and a greater concern that established firms will be disrupted by start-ups.
The survey quizzed senior executives from 57 large corporations, with financial services companies deemed the most data-intensive. Companies from the life sciences, manufacturing, telecom, and online industries also participated.
It shows that big data and AI projects have now become virtually indistinguishable, particularly given that machine learning is one of the most popular techniques for dealing with large volumes of fast-moving data.
Virtually all of the respondents (97%) say they are investing in data analytics, big data, and AI, with 73% of respondents believing they have already received measurable value from these initiatives. That number is half again higher than in the 2017 survey, which suggests that more value is being achieved as companies grow familiar with the technologies.
Consistent with the view that big data and AI are extensions of analytical capabilities, the most common objectives are “advanced analytics/better decisions”. Some 36% had that as their top priority, and 69% of those had already achieved success with the objective. Better customer service and expense reduction are also common objectives.
Just over one-quarter of firms (27%) are pursuing some combination of innovation and disruption, speed to market, or data monetisation initiatives. Data monetisation programmes had the lowest priority and the lowest percentage of success (27%).
However, almost 80% of respondents said they feared disruption or displacement from firms like those in the fintech sector or firms specialising in big data. The technology judged most disruptive is AI, with 72% chosing it as the disruptive technology with the most impact — far more than cloud computing (13%) or blockchain (7%).
Another important and continuing issue is the slow speed with which these established firms make the shift to a data-driven culture. Virtually all respondents (99%) say their firms are trying to move in that direction, but only about one-third have succeeded.
Clearly firms need more concerted programmes to achieve data-related cultural change, the report states, adding that many start-ups have created data-driven cultures from their beginning, which is a key reason why large, established firms fear disruption from them.
One of the approaches that firms have established to deal with data-driven disruption and change is to establish new management roles. However, there is still a lack of clarity about how different data-oriented roles (chief information officer, chief data officer, chief digital officer, chief analytics officer, etc.) relate to each other.
When it comes to the role of CDO, there is substantial disagreement about the major responsibilities of the role and what types of backgrounds are appropriate.
Some 39% say their CDO has primary responsibility for data strategy and results, but 37% assign that responsibility to other C-level executives, and 24% say there is no single point of accountability for it.
The report adds: “Role clarity in senior data-related roles is critical for both leading AI/big data projects and accomplishing cultural change. And while all respondents believed it important, the majority of firms still lack an enterprise data strategy.”
It concludes: “The keys to success are to determine how your firm should respond, assign clear responsibilities for data strategy and results, and then move ahead to execute the needed changes in a systematic and effective fashion.”
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