Managing data-centricity during down-sizing


In my blog post this month, I will be discussing how an organisation can stay focused on improving data-centricity even if it is embarking on down-sizing initiatives. It stands to reason that during tougher economic times it is much more difficult to motivate for an upgrade to data-related infrastructure, an increase in staff, or even for advanced training.

Something I believe in, and that is illustrated by this article on Dataversity, is that the data and business strategies must align with one another. The data in this context encompasses information, analytics, and insights. A company need not cut back on its data-centricity drive in tough times. Instead, it can take on a more targeted approach and not address the entire scope of data and all that it entails.

This is something to consider for organisations who are also not mature in their data-centricity journeys. Taking stock of what is currently available within the organisation is a good starting point to seeing what can be used, what works, and what can be improved upon. It is very much a case of focusing on the low-hanging fruit and achieving quick and meaningful wins. In business terms, this means focusing the data-related effort on both top-line use cases, such as improving customer facing activities, preventing churn and revenue optimisation; and bottom-line use cases, such as process improvement, waste reduction, reduction of inefficiencies and fraud prevention.

Driving data-centricity

I recently came across an interesting EY article that lists the key attributes of what a robust data strategy should contain. Click through on the link to download the paper in full, but here follows a summary of the key points the paper highlighted:

  • Focus on high-priority use cases that create value while moving the business toward data-centricity, with the aim of deploying an accelerated timeline to scale up fast.
  • An outline plan of how data will be managed, including the policies, stewardship, and operating model needed to support data management, must be in place.
  • A high-level, cost-effective architecture that enables the execution of use cases.
  • A plan to increase data literacy across the enterprise and give decision-makers access to data-driven insights to drive value.

Building from here, another relevant piece written by the ARE Corporation lists 11 actions that they use to help organisations steer through challenging economic times. While some of the points have already been covered in the EY article mentioned above, the following four issues struck a chord with me:

  1. Keeping fuel on the marketing engine by doing more with less. If new and even existing customers do not hear from you, they are not going to come back for more. It is not expensive to use data and insights to find the high return areas, such as webinars, blogs, and applications, and to leverage technology to evaluate insights on campaign effectiveness.
  2. Increasing R&D initiatives to validate new ideas and improve process efficiencies and increase revenue opportunities. These do not have to break the bank either as I will explain in the next point.
  3. Developing new strategic partnerships. I want to specifically focus on maintaining a healthy relationship with academia. Universities, research centres and academic hubs have also been keeping up with the pace of change. There are very interesting and mutually beneficial project models which can be undertaken in collaboration with these centres. Not only does the research get done in a controlled, managed, and documented manner, but it is often a great scouting opportunity for talent to take the idea to fruition inside the organisation on completion of the research project.
  4. Relentless focus on removing and reducing waste. One of the most frustrating things for me is the unruly duplication of data all over the organisation. Instead of optimised integration patterns and business-focused dashboards, we see a proliferation of the uncontrolled ‘spreadmarts’.

Overcoming challenges

Of course, all this sounds easy when reading about it. However, getting it right in practice is a different matter. A Forbes article provides some insights into why companies are struggling to achieve this:

  1. No foundation for governed, trusted data. Even with the availability of cost-effective cloud storage, very few organisations have properly governed, integrated repo- and analysis-efficient data together in one place. Furthermore, few put proper data stewardship enablement and process in place.
  2. Apprehension over changing roles and skills. Of course, many people fear or resist change. Staff and leaders must manage the transition beyond policies, systems, and organisational charts to focus on the human element of analytics adoption and turn it into an enabling technology.
  3. Scattered, inaccessible data. My view is that this is a big culprit in blocking the growth to data-centricity. There should be an overarching organisational strategy to reduce data duplication and increase data integration and consolidation.

To quote from the Forbes article: “To enable every person to become a data person, organisations should consider deeper integration between analysis and operational systems. This can be done by pulling those applications into the analytics platform. Ultimately, the goal is to reduce friction and enable more people to make better, data-driven decisions where it’s easiest for them.”

While the points I have mentioned might not cost a lot of money, they do require focus, dedication, and enablement. Even in difficult times, a focussed strategy-aligned, data-centric approach can ensure data plays a key role in enabling the decision-making, revenue generation and cost reduction activities the organisation must make.

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