Trends in Market Intelligence Digitalization
Relevant and accurate market intelligence is the basis of data-driven, evidence-based, informed decision making – Informed Leadership, as we put it at M-Brain. In the Digital Era, market intelligence is more crucial than ever for any company to succeed. You need it to move faster and in a direction that sets you apart from other players, and to find growth opportunities through market creation, instead of playing a zero-sum disruption game.
Digitalization of market intelligence is about making market intelligence an enhancing part of the organization’s digital transformation. On the data and analytics side, digitalization of market intelligence is about moving from intelligence (What happened?) and diagnostic analytics (Why it happened?) to predictive (What will happen?), and ultimately to prescriptive analytics (What should be done? How to take advantage of this future?). Similarly, market intelligence users are moving from generic, historical information to real-time data with easy access and multiple possibilities for personalization.
From AI to VR, from cobots to crowdsourcing, from self-service analytics to digital storytelling – there are multiple digitalization trends affecting and enabling advancement of market intelligence. Our team at M-Brain serves a wide variety of industries and customers in their market intelligence needs, from analysis to designing and developing an organization’s intelligence activities.
We see these five trends as the most fundamental market intelligence digitalization trends today: 1. Data-as-an-asset, 2. Digital biases, 3. One platform utopia, 4. Human + Machine hybrid, and 5. Back to school. Read on to learn more!
There’s more and more data available, and it is easier and more cost-efficient to collect and analyze. Many companies seek to gain more value from data – even getting a unique, competitive edge from an innovative way of utilizing data (hence the mantra “data is the new oil”). However, some thinkers claim it is becoming harder to gain advantage from data. Julian Birkinshaw and Jonas Ridderstråle (2017) talk about the paradoxes of progress, most of which are related to data and “the end of the Information Age”.
- The more we know, the less we understand. We are linear people and companies exist in an exponential reality when it comes to data, innovation and competition. Every day when we wake up and walk to the office, we are a little bit more stupid than the day before. A company needs to develop its capacity to cooperate across the ever-widening ecosystem and to have diverse debates with a deadline. The company needs to make technology its friend: to invite AI to be part of management meetings, and to have cobots supporting daily hybrid decision-making processes.
- The more we connect, the less we can predict. It’s VUCA 2.0. Making sense of the enormous trend space is harder than ever – our operating environment is full of wild cards, black swans, unintended consequences and feedback loops. Companies can’t predict the future. Instead, they need to assume responsibility for creating it. This requires new types of landscape and scenario analysis and the help of real-time information. Although there is more data than ever to base plans on, organizations need to avoid “the more unpredictable things become, the more plans we need to make” -trap.
- The more we know, the more we have to believe. It’s a constant battle between intuition and data, emotion and information. Instead of textbook decision-making process (= gather all the evidence, apply logical reasoning, and end up with a clear judgement), the reality is that we have an intuitive viewpoint, reach a judgment, and then we seek evidence that supports it. Companies need to be aware of biases, and they must bring diversity of perspectives into their decision-making processes. There is no need for the full data set, it’s ok to be ignorant of details – but be sure to have a trusted and legitimate expert to summarize relevant information.
Biases and general laziness in human thinking have been widely acknowledged and fueled by Daniel Kahneman’s groundbreaking studies on the topic. Digitalization brings about new kind of biases that affect decision-making and the utilization of market intelligence. This is shown by false beliefs and biases such as:
- “Finally, our judgments are data-driven as we have access to all data” – Market intelligence is much more than efficient data collection and processing enabled by digital tools. At worst, this bias turns into self-feeding big data hording and cleaning. Digitalization increases the availability and amount of data, and expands analytics options – and in the worst case, it reduces the decision-maker’s thinking time even further.
- “With this AI-based tool, we get the best decisions” – High quality decision making can’t be outsourced to a machine.
- “With our AI-enabled analytics, we are entering bias-free decision making” – Don’t let machines fool you. They, too, have biases. After all, AI is mostly based on us teaching the machine, which means that human judgement errors are transferred to them.
Most companies and business people dream of “that one, unifying tool, for all our business and market data”. It would be on-demand, mobile, real-time, with a great user interface and experience. This is, however, an ever-elusive dream. In reality, we are getting into what Kevin Kelly calls “remixing” of data formats, sources and technologies, datafication, and the Internet of Everything. Technological solutions and trends come and go – just review Gartner’s Hype Cycles and Magic Quadrants.
Technology has been an enabler for business ever since the industrial revolution. With sophisticated digital technologies, AI being one of the hottest of them now, expected impacts vary from “AI as a business problem-solver” to singularities like “machines taking over from humans”.
Paul Daugherty and James Wilson (2018) talk about the 3rd wave of business transformation, adaptive processes, where digitalization leads to a symbiosis between man and machine. Technology is complementing and augmenting human capabilities as humans and machines collaborate while improving business performance. Daugherty and Wilson call this “missing middle” as only a few companies have understood the possibilities of this symbiosis. Most companies still consider technology and human performance as existing in separate silos.
Many opportunities to take business performance to totally new and improved levels are missed when, for example, in market intelligence digitalization is seen as choosing “the right” software solutions, Instead, the available technologies should be seen as enablers, tools that analysts, decision-makers can use. As Eric Siegel (2018) puts it, you should not lead by software selection – it should be the team skills that come first.
The most important trend in digitalization of market intelligence is the need for continuous learning – the needs of competencies are drastically changing. The World Economic Forum urges organizations and individuals to seek continuous uptraining, “establishing a system that would allow every employee to devote significant time – every week, every month or every year – to acquiring fresh skills”.
Heads of leading companies are loudly promoting this life-long learning approach. Microsoft’s Satya Nadella summarizes it like this: “Don’t be a know-it-all. Be a learn-it-all.” Nokia’s chairman Risto Siilasmaa leads by example. He went back to school himself to become AI-educated, and based on his experience he shares “The Five Steps to AI Competence” template for others to follow.
In market intelligence it is not enough to just have world-class analytics skills – the digitalizing world requires to deeply human-only skills like empathy, creativity, and ability to collaborate in exponentially growing ecosystems. We are moving from “know-how” to “know-who”.
Which market intelligence digitalization trends affect you most? Do you want and/or need to start re-thinking your market intelligence? Are you ready to explore the possibilities digitalization might offer?
Digitalizing market intelligence is much more than just digitalizing market research or choosing the right tool or platform. It is about changing the whole mindset and fostering a new way of utilizing market intelligence in an organization.
Towards Smart MI: M-Brain can help you rethink your whole market intelligence ecosystem, from users to partners, from platforms to customers, and we can help provide the solution to the difficult questions of what to digitalize and how to do it. With our decades-long experience and in-depth expertise in market intelligence, we will make sure that you will succeed in making the most of your market intelligence. Contact us to learn more!
Bennett, Nathan & Lemoine, G. James (2014): “What VUCA Really Means for You” Harvard Business Review
Daugherty, Paul R. & Wilson, James H. (2018): ”Human + Machine – Reimagining Work in the Age of AI”
Golden, James (2019): “AI has a bias problem. This is how we can solve it.” World Economic Forum
Kelly, Kevin (2016): ”The Inevitable”
Helkearo, Sanelma; Kärkkäinen, Nora; Mäkinen, Soile & Miao, Shiyu (2019): “Smart Market Intelligence” M-Brain
Mauboussin, Michael J. (2009): “Think Twice – Harnessing the Power of Counterintuition”
Mullany, Michael (2016): ”8 Lessons from 20 Years of Hype Cycles” LinkedIn
Mullany, Michael (2018): ”8 Lessons from 16 Years Business Intelligence Magic Quadrant” LinkedIn
Reif, L. Rafael (2018): ”A Survival Guide for The Fourth Industrial Revolution” World Economic Forum
Siegel, Eric (2018): ”3 Common Mistakes That Can Derail Your Team’s Predictive Analytics Efforts” Harvard Business Review
Siilasmaa, Risto (2018): ”The Chairman of Nokia on Ensuring Every Employee Has a Basic Understanding of Machine Learning — Including Him” Harvard Business Review