Portfolio Management For Data & Analytics

Portfolio Management For Data & Analytics

Axel Suetens 2 comments

Gartner expects the Data & Analytics market to rise to $22.8 billion by 2020 and Reuters foresees additional growth to $29.48 billion by 2022 [1]. According to the Project Management Institute, demand over the next 10 years for project managers is growing faster than demand for workers in any other occupation [2]. With both these disciplines growing at an explosive rate, the toughest challenge will be in the realm of project portfolio management.

Surviving in an era of digital Darwinism

The past years businesses have matured tremendously in the digital competition to stand apart from rivals and respond faster to the marketplace. Adapting to increasingly digital market environments and taking advantage of digital technologies to improve operations and drive new customer value have become obvious goals for nearly every contemporary business. A multitude of additional technology initiatives and projects were started to meet the increasingly demanding expectations of the market. Obviously this urged CIO’s of any size of companies to balance out loads of initiatives and projects, all with their corresponding business cases and ROI targets. A dollar can only be spent once, so needless to say that solid and sound portfolio management practices need to safeguard the coherence of all investments. But when we’re talking about Data & Analytics, this is where the shoe still pinches in too many companies.

Portfolio Approach to Data & Analytics

Data & Analytics solutions are mostly never built in one single effort. In fact they’re best in class examples to design and develop in a flexible and iterative manner to avoid lengthy development cycles, respond to changes quickly and focus on a maximized user engagement. Typically these projects are done individually and in an isolated manner and on occasion they are fitted into programme because of their relationship with each other towards a specific goal.

It is a reality that many different Data & Analytics initiatives are usually run by many different departments or functions in parallel, resulting in quite nasty and costly deficiencies such as shadow IT, contra productive goals, competition for resources, redundant and wasted effort and so on. A lot of time and money can get lost if data driven projects are not aligned with initiatives and projects in the space of master data management or data integration or going cloud or with new kids on the block like artificial intelligence. To only name a few!

In order to capitalize on your organization’s Data & Analytics initiatives it is key to apply an efficient portfolio management process that integrates smoothly into the existing organizational structures.

And yes, of course you’re right, there’s no single right way to do IT portfolio management but a strong portfolio management process can turn all that around and do the following:

  • Optimize value of Data & Analytics investments while minimising the risks
  • Break down the barriers and improve communication between IT and business leaders
  • Encourage business leaders to take their bit of responsibility for data driven projects
  • Allow companies to allocate and schedule resources more efficiently
  • Make it easy to abort or reduce redundant or low value projects.

Portfolio Management: The Cradle of IT Value

Portfolio management is a good thing but getting to an ideal state requires a serious commitment from both the business and the IT side. Directing an individual Data & Analytics project correctly will ensure it will be done right. Fine. But directing ‘all data centric initiatives’ in an aligned and successful manner will ensure that you’re doing the right projects. To make that happen, companies need to be organised on at least a couple of levels:

  • Increase collaboration: IT should be able to detect data & analytics demand signals in the business
  • Faster time to delivery: The business should feel confident to share their data & analytics wants and needs with IT
  • Streamlined roadmap: All data driven projects should be mapped into clear and transparent ‘maps of meaning’.
  • Increase project delivery success: Measure and score the outcome of your Data & Analytics projects against their business cases

Sounds too easy right? But doing it properly is not so easy. First we need to understand what the typical mistakes are that companies make when driving a portfolio management initiative. Let’s look at 5 common mistakes which can turn project portfolio into failure:

  • No tangible investment strategies: It is a common practice in many companies, whether start-ups or larger corporations, to directly start with budgeting and funding. The projects scoring higher on the priority list are picked off based on the budget until the funds have been completely exhausted. The remaining projects are simply postponed or backlogged. But have they achieved the broader business goals? Did they contribute to the overall strategy?
  • Managing projects only on atomic level: This will prevent organisations of keeping track of the bigger picture and map it to the company strategy. Putting together meaningful and coherent programs and breaking large projects into smaller, manageable pieces will only support better communication and decision making.
  • Not prioritizing projects and/or tasks: Many departments have multiple, concurrent projects running, for both internal and external customers. Only when your organisation is able to translate the determining parameters into clear KPI’s that are used and understood across the company, a dialogue can be maintained that will result in the proper selection of things to do and things to drop.
  • Letting changes get out of hand: Scope creep is pervasive in project management and difficult to manage because, as the name suggests, it creeps up on you. Without the proper control, it can severely affect your project success and it should be curtailed by a strong project portfolio process.
  • Not using a project portfolio tool: Usually because many project tools are already used and there is not much appetite for an additional software burden and cost. However there’s good evidence of the greater chances of success if organisations use a PPM tool. For example, a 2009 survey by the market intelligence provider IDC reported that organizations successfully implementing a project portfolio management tool saw project failure rates drop by 59%, spent 37% less per project, reduced the number of redundant projects by 78% and increased resource productivity by 14%.[3]

To conclude and to make a case for portfolio management and Data & Analytics, Peter Drucker once thought us that you can’t manage what you can’t measure. But you can look at it from another angle as well. Key Performance Indicators (KPI) don’t magically fix blunders, they provide companies contextual information and insights that will help them achieve their objectives. But bear in mind that portfolio management KPI’s are not equal, they can work on one organization, but not on the other. When companies integrate analytics in their portfolio process they need to understand the overall aspects, individual intentions and general objectives.

And there is no mistake about it, this is much like a soul-searching phase for both the business as well as IT.


[1] https://selecthub.com/business-intelligence/business-intelligence-software-market-growing/

[2] https://www.pmi.org/learning/careers/job-growth

[3] IDC Podcast (sponsored by PPM vendor CA), “Measuring the ROI of PPM (Project and Portfolio management)” March 20, 2009.

2 Comments

Joris Van den Borre

October 23, 2019 at 11:14 am

Totally agree on this. As analytics broaden up and the era of the siloed data warehouse seems to be long gone, we are embracing public cloud at high pace. To maximally achieve benefits I’m assured we need to reconcile pragmatism with opportunity, managed at a consolidated level. Do start with a clear strategy – eg buy before build – before you embark on exploration, and ensure to capitalize on learnings and investments as you go. Good luck !

Michael

December 7, 2019 at 1:44 pm

By combining multiple data sources, you can increase the dimensionality of models and solve complex optimization problems that account for hundreds of individual portfolio factors. This allows portfolio managers to suggest tailored investment plans to their clients both in B2B and B2C operations.

Leave a Reply to Michael Cancel reply

Your email address will not be published. Required fields are marked *

* Checkbox GDPR is required

*

I agree