When faced with a crisis, not all businesses will survive.
The past year has highlighted the importance of making decisions that are informed. Arguably, the most prominent example of this is the UK government, who are claiming to be “led by data, not dates”. It is a rare occasion that a business executive does not recognise the vast amount of value that is hidden in data.
For a long time, businesses have been on their own journey to fully understand and exploit the value of their data. However, over recent years, many have found that to get the fruits, they must first have the ‘roots’. Even some of our own clients tell us they don’t fully trust their own data. Huge investments have been and are currently being made to build and nurture data ecosystems that once in place, will allow data to be joined up and fully exploited by advanced analytics and AI to drive maximum (and long-term) commercial impact. Many businesses have data roadmaps that span years.
However, there is nothing like a crisis to shake up the best of plans. As a result of the pandemic, almost overnight, customer behaviours, supply chains, business operations and logistics were transformed, some possibly irreversibly. The need for insights from data to better understand business health and changing customer needs has been heightened without the luxury of time to perfect the foundations first. So, how can businesses accelerate their analytics programmes to deliver rapid, business-changing insights to navigate periods of uncertainty, while their roadmaps continue to support the longer-term trajectory?
It is a rare occasion that a business executive does not recognise the vast amount of value that is hidden in data.
This article provides some guidance to business leaders needing to stand up analytics on their data immediately, rather than waiting on the roadmap for large-scale data foundations to be in place first. It is these organisations who will emerge as winners in the ‘new reality’. Businesses need to demonstrate the art of the possible.
1. Start with the business priorities
Often, the main obstacle is that companies start from the wrong end of the problem: the data. We have worked with organisations that have, for example, built a data lake before looking for insight. They tend to find gaps when aligning them with their objectives.
The answer is to start with the questions the business needs answering (or the objectives it needs to achieve), then gather the data needed to help answer them. Bring data sets together as efficiently as possible to enable the targeted analysis required to provide the insight that will answer these questions.
At NTT DATA, when we start an analytics project, we hold a collaborative half-day workshop with our client to fully understand, and frame, the business problem(s) or objective(s). Getting this alignment from business leaders up front is the rudder that guides the entire data gathering and analysis approach.
2. Target the ‘low hanging fruit’
Once business priorities have been agreed, organisations can start to explore what data they need and what analytics can be carried out to address those priorities. Typically, gathering large and sometimes complex data sets takes time. Therefore, businesses should weigh the time taken to acquire data against the business benefit. When delivering our ‘Next Generation Business Intelligence’ service to clients, we always start with use cases that can be delivered against high priority objectives in the fastest time, to deliver rapid insights. This immediately brings to life the power of data in informing decisions, while providing a ‘running start’ to scale out to the rest of the business.
There were examples of this when the pandemic first hit. Manufacturing plants reopened shop floors by looking at historical machine operating data, to review what aspects of operation and maintenance could be automated. To allow some people back to work, offices combined floorplans with airflow and infection models to redesign layouts.
3. Embrace agile working practices
Siloed data and business functions are some of the most common barriers to scaling analytics that we encounter across industries. Many of our clients express concern about operating models being able to meet shifting strategic priorities. However, the move from siloed work to interdisciplinary collaboration is often highly impactful. Standing up cross-functional teams with a common objective and all relevant stakeholders means that analytics solutions can be delivered in a matter of weeks, regardless of analytics maturity.
Agile delivery methods can produce minimum viable products in the form of dashboards or models for specific use cases. Incremental development frees teams to test ideas rapidly, gain early user feedback in the process and iterate. Collaboration and communication can be more powerful than the most advanced technology and business processes. The long-term EBITDA growth for truly agile organisations is 16% compared with 6% on average for non-agile organisations.
4. Get comfortable with data imperfections
Leaders need to acknowledge that, while existing data may be incomplete or imperfect, it can still generate useful insights – if used with a healthy dose of human judgment. With data, it can be easy to become fixated on the limitations of the data rather than the end goal. We often talk about the 80-20 rule with clients, who usually prefer an 80% correct answer from data insights, because time is of the essence and waiting for the additional 20% of analysis usually takes 80% of the time!
Almost overnight, customer behaviours, supply chains, business operations and logistics were transformed, some possibly irreversibly. The need for insights from data to better understand business health and changing customer needs has been heightened.
Some retail organisations have taken this approach when sourcing third-party data sets to understand more about their workforce availability during Covid. Often these data sets can be large and unstructured, however analyst teams have quickly managed to merge qualitative data, such as how employees would likely commute to work, with internal workforce data. This has for example allowed organisations to anticipate when and where they need to adjust their workforce plans.
5. Don’t be afraid to outsource to accelerate
Legacy and cumbersome infrastructure can often hold companies back from undertaking valuable data analysis. If their technology estate is a real barrier to progress with analytics solutions, businesses should consider using a third party to generate data insights and enterprise-wide reporting for them – at least in the short term. At NTT DATA, we have found that some clients face this challenge, so we created our own analytics managed service as a way of helping them rapidly accelerate analytics and insight generation. This could transition to an internal capability over time. Impactful business insights gained as a result could enable faster, more informed decision-making and be the difference between thriving and just surviving in a precarious global environment.
History often reminds us that crises can present opportunities. Leaders who can make data-driven decisions based on analytics that can be stood up rapidly will be in a stronger position to navigate through the near-term challenges that the pandemic has raised. At the same time they will gain learnings that can be applied to embed AI and analytics over the long-term. The road to recovery is marked with data, and those companies that can best harness it will set the pace.
