The need to respond quickly to new business objectives

The business objectives will change as we invest heavily in digital technologies, as we increasingly recognize and embrace this changing world where digital knowledge and insights begin to challenge and change our existing frameworks of innovation thinking.

Part five of a seven-part series

The outcomes of the investment are expected to provide clear returns and these might include but are not limited to:

1) different customization of services 2) quicker response to market trends in new offerings 3) identifying real-time cost optimizations, 4) concentrating on faster, more accurate decision-making to give new competitive edges 5) better and more holistic R&D 6) automating even further the supply chain management, 7) alter your approach channels to market, 8) move your business into new adjacencies or even white spaces and finally 9) design new business models and value propositions.

There will be lots of new moving parts to grapple with to be future innovation agile.

Equally, our senior managers will be digitally challenged

As machine learning or digital inflows occur, the pace will become even more rapid and the leaders will be called upon to create the new innovation forms. The mental capacity and physical adroitness will continue to multiply through digital technologies and the need to be agile in responding. There is going to be significant advances in cognitive work but will this be human or digital brains doing the work?

If we foresee a deeply driven digital world, the new place for human intervention is “where do I actually add value and where do I need to get out-of-the-way and let data take me?”

So we are going to be potentially more data-driven, less intuition driven. Your whole domain expertise will eventually be stripped away.

The challenges within the leadership of our organizations are to somehow envision how the physical world and digital world can be ‘pulled’ together.

That critical need of applying the different forms of intelligence and experiences and provide the ways to network and build from these ideas and insights into tangible values that fit with the organization’s objectives.

The key to leading in a data-driven world recently was suggested in a McKinsey article on the Manager and the Machine will be 1) learning to ask good questions, 2) attacking exceptions, 3) tolerating more ambiguity and 4) focusing as much as possible on employing ‘soft’ skills where insight gets turned into messages that resonate with the organization to see as innovation potential will require that human touch.

How will digital automation improve and balance our innovation management?

As data flows in we will need to rationalise the decision-making, we will be seeking out for the gathering of data more fact-based observations. The intervention of personal opinions and “experience” will become highly challenged and hopefully on the endangered list.

We can look to insights as possibly better predictions of success by lessening the risk factors of no knowledge or very limited understanding and underpin the assumptions with ‘trends’ and rich insights to validate directions and emerging solutions

We will be able to explore the ‘worlds’ trending on those topics relevant to our needs, knowledge and innovative thinking. Exploring consumer behaviours, technology trends, competitor monitoring and significantly granular insights to shifts taking place in real-time and near-time.

We might discover the unexpected and the surprising and not rely on instinct, sixth senses or ‘our past experiences.’ This may increasingly clash in the validating, we need to ensure this increase in insights will yield better ideation. As time goes by and we become more reliant on digital technology our idea management thinking will become distinctly different, less based on a hunch, feel or (sadly) experience.

We will be even more under pressure as if the data is ‘open and common’ for all to ‘trawl and gather’ then you are in a race to capitalize on this before your competitors is able to learn or react to it.

Through validation we can constantly confirm or question the business case, and we can test hypotheses and experiments. We can check assumptions for validity far more and across broader communities than in the past.

Having data, and creating a history you can equally be more backward looking to judge success and failure and use these different understanding and analyses to design more predictive forward-looking models and framing techniques that capture differently or improve your pattern recognition.

The growing science and value of analytics for innovation

Innovation analytics will become an integral part of the entire innovation life-cycle to make smarter decisions and to optimise the innovation performance along its value building chain.

Having clear data-analytic strategies that break down the information into something commercially useful will require more searching for ‘weak signals’ that contain those real nuggets of insight.

The early mover has to have a speed and pace of decision much higher. The emergence of customized dashboards will dramatically advance but working on the parameters that feed into these will be a real challenge.

The need to analyse, synthesize, transform into actionable events and value-enhancing insights will be based more on fact-based decisions but the human interventions become more responding to exceptions not stage-gate managing everything.

Analytics will evolve into providing answers or guiding you to create descriptive answers, situational to why this is happening, predictive in what this might mean or trending towards, prescriptive in validating choice options and finally simply evaluating the risks, the choices of doing something or not due to this data and insights.

Adjusting our thinking on risk

Technology insights can provide a better mitigation risk, it can equally fine-tune innovation but it can equally make it far more complicated. Massive amounts of data can complicate decision-making, determining the right decisions and next steps.

Technology will require opening up the present risk mitigation process to capitalize on emerging opportunities at greater speed and higher levels of compromise, to meet that emerging opportunity ‘seen’ requiring it to be turned into commercial offering quickly, so competitive advantage can be gained.

Getting the parameters right, this digital innovation criteria will potentially determine the direction of the organization and where it places its resources.

McKinsey talks of democratizing and managing will be determined more by pattern insights, and clear insights, less upon waiting for a top manager’s decision as the defined strategy and business development directions become sharper, enabling this ‘decision-making’ to be made much closer to the market or the information source.

The urgency of Leadership

Leadership will have to become far more effective in decision-making and digitally savvy, so as to provide the guidance and strategic frameworks required for the governance and direction digital technology is required to go, to meet the organization’s stated objectives, and those will change constantly as better insights flow in. Defining those needs and the expected returns does sharpen everyone’s minds.

Tom Davenport of Babson College and MIT made some helpful suggestions about the general use of the phenomenon of big data, and add its project attributes so leadership understands its increasing value.

He suggests these are 1) describing the data you are using, 2) where the data comes from, 3) the problem you are wanting to solve and 4) wherein the company you will use the insights to solve what. This as he suggests “provides clarity” and we all need that with the present easy use of “big data,” which impresses no one.

The changes wrought by applying all the different digital technologies promise far more than the adoption of a more fluid core that Haydn Shaughnessy has written about,  as “understanding a new hierarchy of enterprise needs.  Adopting a new, adaptable infrastructure where technology, humans and competitive conditions intersect and can interact in real-time.

Leadership needs to respond quickly to the demands and additional strains this will place on their organization to provide guidance, strategic direction and above all the right resources to capitalize on all the changes this will bring. To expect to achieve a ‘real’ return from the investments being made or asked for in new growth and innovating value.

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*** This is the fifth of a seven-part exploratory ‘open thinking’ about digital technology and its potential impact on innovation as we know it today. These will be published daily over the next week. The intent is not to provide definitive answers so much at this stage, it is more about raising our thinking.***

* Sopheon through its software applications have been outlining some of their arguments for improving innovation due to big data and analytics and these helped me kick start part of my own thinking here.