If you offered any boardroom free access to 10 times the amount of data that they currently have access to, almost 99% of them would take you up on the offer. C-suite executives are so used to hearing about the power of data and analytics for driving profit that the equation has been reduced to: more data = more revenue. This is no surprise, given the global big data analytics market size is expected to be worth USD 549.73 billion in 2028.
However, even if we ignore the (sometimes colossal) costs incurred by companies that are paying service providers to gather more data for them, there is another gaping hole in the logic of these decision makers. More data can only generate value if you know how to use it. Data for data’s sake is completely pointless, and organisations are increasingly jumping the gun when it comes to pursuing ambitious insights strategies, without the know-how to make the most of new data. AI is thought to be the go-to solution for this, but this is probably where the biggest misconceptions come in; AI is not a thing in itself that just ‘works’ – algorithms need to be trained and to do that, you need proper strategic objectives and an understanding of what AI can really do for you.
In addition to all this, there are the added security risks that come with impromptu decisions to bring on more data; if other elements of your IT strategy are not altered to accommodate for the influx, then the risk of mistakes such as data leaks and breaches goes up substantially. PwC’s 25th Annual Global CEO Survey identified Healthcare, IT & Telecoms, and Financial Services as three of the most at-risk industries when it comes to cyber attacks. It’s clear to see why – these industries are absolute data powerhouses, making the surface area for attacks huge, whilst the size of the industries makes the potential financial gain for hackers a lot higher, too. In other industries such as urban planning, the risks come from the type of data collected. In these projects, data tends to be visual, making personal privacy an even bigger concern. Taking on more data increases the chances of hackers targeting your business, as well as introduces more compliance demands, therefore it’s imperative that business owners really think through the decision.
However, this is not to say that data and analytics is NOT the future of business – it most certainly is one important element of it. But rather, CTO’s need to be taking greater care over their cost-benefit analysis before looking to data analytics to improve their business models. Not all data will be useful, and you need a solution that is built from the ground-up to be compliant and secure.
More data, more confusion
Working with customers who want to explore how they can use data and analytics to derive powerful insights for their business requires you to educate decision makers on a daily basis. This surprised me initially, and I constantly found myself asking how these companies could be looking to spend millions on a strategy which they didn’t know how to execute.The answer turned out to be simple; because everyone else was doing it!
Spending on data has been on a steep incline for the last decade, and analyst house IDC predicted that global spending on big data and business analytics solutions would reach $215.7 billion last year. The market is huge, and business leaders have been terrified of getting left behind.
However, the reality is that data and analytics strategies have been poorly planned, leading to a lot of confusion within IT teams. These teams either don’t have the training to extract useful insights from the datasets, or they do have the skills, but are not given a clear enough brief. Time and time again, companies have tried and failed to implement analytics strategies, falling down every time it comes to extracting real value.
When you add the increasing demands of IT security on top of this, with cyber threats soaring, new privacy rights coming into effect, and more legislation being passed on the use and sharing of this personal data, it’s easy to see why VentureBeat reported back in 2019 that 87% of data science projects never made it into production. Since the pandemic, the security problem has only gotten bigger, and so has the spending.
Defining strategic objectives
It is clear then, that data managers need to take a step back to review their strategic objectives before taking on tech that promises more data, insights and analytics. Doing so without looking at the bigger picture creates more problems than it solves. In my experience, businesses require serious amounts of guidance before taking on more data and next-gen technology like AI for analytics, and there are two elements which are essential for execution: the right platform, and the right talent. Consultation is becoming a massive field in the IT sector for exactly this reason, with a growing number of these professionals coming from a data science background.
Understanding that quality data collection is a time-consuming but important process is essential if you want your AI to learn. Having a team of people who are real experts in this space is the only way to make cultural changes in your IT team where the process and end goals are truly understood. AI needs a lot of data, and the success of it doesn’t rely on the algorithm so much as the quality of that data, so it’s a great example of where people fall into the trap of thinking more data automatically generates more value and revenue.
Collecting more data without #duediligence, getting the right team, and the right #AI or processing tools, is a massive gamble. You risk spending a lot of money and creating more #security risks, without any of the pay off. #respectdataClick to TweetCollecting more data without doing due diligence, getting the right team on the ground, and the right AI or processing tools for that team to use, is a massive gamble. You risk spending a lot of money and creating more security risks, without any of the pay off. Business culture has already started to change in response to these risks, but the further growth of consultation is going to be an important prerequisite to a future where the value of data is really unlocked.
