Management Challenges in the 21st Century
The rapid advancements in technology have the potential to both create opportunity and drive innovation. AI/machine learning is just scratching the surface of what can be achieved through process automation and advanced customer engagement.
However, the hard facts are that most businesses live with a complex mix of technology and data. There can be a variety of systems and data which can be disparate, complex and understood by only a few. Combine this with a mix of on-prem and cloud-based computing and it is easy to see why many analytics projects are under-delivering.
As you would expect, we see both good and bad examples, with the main issues still being poor data quality with key parts of analysis and presentation stuck in spreadsheets. Poor communication between departments can also mean that invaluable external resources and insight that could add value to many areas of a business are not shared.
Looking to the future
Data governance will become an integral part of business strategy, recent Gartner research predicts through 2022 over 75% of data governance initiatives will not adequately consider AI’s potential security risks and their implications, resulting in quantifiable financial loss.
Ethics and privacy in the digital landscape are growing concerns with the advancement of AI/ML and the lack of regulation-making tech the last wild west. In the next 5 years, 30% of government and large enterprise services using AI will require the use of explainable and ethical AI (Gartner) which is why this should be considered now and form part of any businesses data strategy.
People-Centric tech
For 35 years we have advised organisations on how to put their people first and this is still the case, how does the tech impact your employees, your customers, your partners and increasingly bring purpose to your business’ role in society. Rather than building your tech stack in isolation you need to look at your business strategy and how the tech will help you achieve your business ambitions. Change should be incremental and crucially choose your tech solutions carefully. There are literally hundreds of powerful tools and systems which promise the earth but what is often overlooked is these need relevant expertise and training to succeed. This user-led environment can be challenging, and we reiterate in the data world there are no silver bullets.
Avoid Unnecessary Risk
A great place to start is by commissioning a proof of concept (POC), these are relatively cost-effective, should take a short time and will give the business assurance that the project will deliver. Often a POC will highlight areas of the project which may need developing or refining and test that a system or tech solution will work. When working through the POC it’s important to keep checking what the objectives of the project are and refining the scope. Data transformation can be hugely challenging and with so many data projects ending in disappointment, the POC is an ongoing and essential tool.
If you’re just getting started
- Take the time to understand what tech looks like now, and constantly revisit
- Define what ‘good looks like’ when using data across your business and don’t forget the people, processes, tech and behaviour
- Be realistic as to your current levels of data maturity – you may need to go back to basics
- Outline the options for how a future ‘data management team’ could operate
- Identify key areas of focus and early potential ‘proof of concept’ candidates
The benefits to the business
- By ensuring your POC is practical, value can be proven quickly
- This will help achieve senior-level buy-in and ensures the business can adapt as it becomes a more informed customer
- Alignment and improvement of underlying business processes
- Improved forecasting
- Enhanced profitability
One thing is certain, the speed and advancements in technology are here to stay and IT leaders must adopt the mindset that change is the only constant and they need to start to more rapidly advance their tech and data strategies. Tangible results can be quickly generated by generating small proof of concepts which can become business critical. However if the projects are too ambitious and run in a siloed unsupported way, they will be left gathering dusk and risk in the office of the CTO.
