A new study by technology research specialists Vanson Bourne has discovered that many enterprises are failing to use their data effectively or make data-driven decisions, though many are planning to invest in AI to combat the problem.
The 2018 Data Value Report examines the business opportunities presented by data, polling 500 enterprise IT decision-makers to determine their investment plans and data priorities, along with the hurdles they face.
It found that most organisations are only just scratching the surface of realising their data’s potential. On average, organisations are using only half (51 percent) of the data they collect or generate, and data drives less than half (48 percent) of decisions.
The research, announced by self-service application and data integration company SnapLogic, also revealed that enterprises plan to invest an average of $1.7 million in operationalising their data over the next five years – more than double their current spend.
The data goldmine
The IT decision-makers polled for the survey estimate that this investment would reap an annual revenue increase of $5.2 million, with organisations seeing a potential 547 percent return.
Sixty-nine percent of participants see customer data as being the most valuable, followed by IT (50 percent) and internal financial data (40 percent).
In light of the results, Gaurav Dhillon, CEO at SnapLogic said:
There’s a saying that every business must be a software business, but what they should really focus on is becoming a data company.
“Businesses understand that dedicating time, money, and talent to data will lead to long-term revenue gains, yet in reality most enterprises are still far from generating significant value and ROI.
“Legacy systems, tedious manual labour, and the sheer volume of information are preventing organisations from maximising their data-driven potential. The enterprises that act now to spread data literacy throughout their business will be the ones to thrive.”
Legacy laggards
One of the biggest obstacles preventing businesses from efficiently using data is the work and resources required to process it. Those surveyed spend 20 percent of their time manually working on data and preparing it for use. This includes manually integrating datasets, applications, and systems, as well as implementing and managing APIs.
Eighty percent admitted that outdated technology is holding their organisations back from tapping into new data-driven opportunities.
Despite the clear business case, organisations are currently failing to take advantage of obvious opportunities. While 98 percent of respondents are planning or in the process of digital transformation, only four percent reported being ahead of schedule.
Many companies are overwhelmed by the amount of data they collect. Seventy-four percent admitted to facing unprecedented volumes of data, while struggling to generate useful insights.
This is compounded by poor faith in data quality. Only 29 percent of organisations have complete trust in the quality of their data, either due to inconsistencies in formats, inadequate tools, challenges with interdepartmental sharing, and/or the sheer volume of data.
Is AI the answer?
AI is seen as a potential solution to the enormous challenges presented by data volume, quality, and processing. Eighty-three percent of respondents see scope for the technology in helping to tackle these problems, with 27 percent already investing in AI and machine learning, and 56 percent planning to do so.
The majority see AI as key to enabling them automate data analysis (82 percent), data preparation (73 percent), software development (66 percent), and application integration (63 percent).
Internet of Business says
With the majority of IT decision-makers drowning in data and held back by legacy processes, AI could be the wisest route forward, and many are more than willing to employ the technology, says the survey.
The vast quantities of data produced or collected by companies may now be too great to digest by any other means. AI can excel at spotting trends and opportunities in big data, while machine learning can take this a step further by fine-tuning processes as it is fed with more data – garnering insights that a human analyst might otherwise miss, or at a scale that would otherwise be unobtainable.
Meanwhile, the prospect of a significant percent return on investment should see board members rubbing their hands with glee, so creating a business case for AI-powered data analytics shouldn’t be too great a challenge for many IT departments.
Many companies have likely been too preoccupied with GDPR (and California’s similar new laws) to spare a thought for how they might better leverage their data. Any new AI-based processes will need to be informed by Europe’s new data regulations, should the organisation wish to operate in the region.
Nonetheless, the data mine gleams with untapped profits. But while the opportunities may be great, our growing reliance on opaque machine learning algorithms raises serious data bias concerns.