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Imagine this scenario. The year end is looming and your Chief Sales Officer is busily looking at the sales and pipeline numbers for the company’s key accounts. You need a strong quarter close to finish the year in a position to enable your growth plans for the following 12 months. The sales teams are flat or reacting to customer requests and qualifying new opportunities, meaning you have limited resources for new business development in order to close out the quarter.
Where do you focus those teams for the most impact? Decisions like this often come down to gut reaction or mining an existing customer relationship. The quarter may close positively and meet the sales goals. Consider these two questions, though: is this a long term, repeatable, and sustainable business model? Was the sales opportunity chosen for critical pursuit really the one with the best return or did you miss a greater opportunity?
Using data to strengthen trust with your customers
The most successful relationships are where suppliers anticipate their customers’ needs. Customers get value from that. When working with a knowledgeable partner, they trust that past experience will lead to better service and increased satisfaction. This often came from that trusted relationship with that one sales person who has been partnering with them for many years.
However, there lies the risk. A single individual is single point of failure. What organizations need is a way of getting that same (or better) level of insight from the data generated with each customer interaction. This enables not just the account managers and sales people to make these predictions, but supply chain, logistics, manufacturing, and customer support to be absolutely aware of what the business demands in order to satisfy every customer. When it is data and not individuals, you have insights that scale.
Data can also paint a big picture in a way that no one person is capable of. This is a great way of seeing where gaps and opportunities might emerge that even the customer might not be aware of. Leveraging this data means that you not only meet your customer needs, but exceed their expectations and quickly establish your company as a trusted partner.
Using data-centric principles to create a sustainable business model
As a second example, look to manufacturing and specifically at plant and equipment efficiency. Here, an understanding of how to direct the flow of work through the factory line means that workloads can be intelligently scheduled and implemented. Again, this is a good example of where personal experience and intuition often direct proceedings.
However, being able to predict and understand the impact of malfunction or maintenance on that flow—and then prescribe the optimal response—can mean optimizing the cost of production and increasing margins. Understanding these things quickly can results in smaller impacted batch sizes and quicker time to recovery. This goes beyond predictive data-driven outcomes and adds a reactionary capability. Removing the dependency on an individual and applying that expertise through data and analytics at plant scale drives that sustainable and repeatable model that is the hallmark of a data-centric organization.
Three core principles for building data strategy
Holding data in silos and focusing it only on specific areas, where it is created, reduces the business-wide impact effectiveness of that data. It also reduces its overall value. Today’s successful organizations must have a data strategy that follows three core principles. Leveraging these three principles allows execs to create new business models that form the backbone of an organization and leadership team that not only understand every context their business operates in, with tangible and quantitative evidence, but can also quickly adapt and realign to new challenges and create new opportunities.
Private and secure—Personal data of partners and customers must be kept private and should not be compromised for any reason. Exposing personal data not explicitly agreed to is the fastest way to lose the trust of that person or organization. It can also be a financial and regulatory exposure. Personal data must be handled with this intrinsic to data management. It can be leveraged through anonymization or aggregation in order to provide value to the wider business, while always keeping data from individuals secure.
In order to pursue this principle an organization must be fully aware of what data it holds and why. This is the first stage in understanding the data eco-system and catalogues data sources, their owners, and business imperatives.
Discoverable and dynamic—while maintaining the first principle, data within an organization should be discoverable in order for it to be applied to more than one outcome. Very often, data from one area of the business can provide valuable insights to other areas. If the interface between those areas are people who need to take time to understand the request, are open to misinterpreting the need, or simply do not have a wide enough view of the business to understand the additional context for that data, the value is depleted, and the data remains locked away. Decision-making requires context and that necessarily has to be garnered from multiple data sources. Once discovered, this data needs to be accessible to plug into those new scenarios and use cases, without impeding its existing uses and without compromising the first principle. This dynamism is the foundation of agility in a business’ ability to react.
The implementation of this principle is really an extension of the work done to support the first. It involves creating data dictionaries and meta-data describing the data and how it came to be. This should cover the whole provenance of the data and not just its initial state, including any post-processing, cleaning, or transformation activities that might influence its resolution or accuracy.
Available where needed—availability from an IT perspective is often viewed as simply having the data accessible. This means the storage device that contains the data is operating and serving information with an architecture that provides resiliency to failure. But it is important to realize that data now exists beyond that single storage domain. It exists across a plethora of devices and in many different environments. For the manufacturing scenario outlined above, the data needs to be available on the factory floor. Often referred to as data and analytics “at the edge,” it provides the most value when present where the customer contact and business execution are happening. This can be at the location where the data is captured, or where other interactions are creating new data that needs the context of existing sources to be meaningful. The ability to make data global while still preserving security and privacy is the final principle that unlocks a data-centric organization.
In order to implement the availability principle you need to look to traditional IT service methodologies to understand the protection requirements, the mean time to recovery required for any services depending on this data, and the implications of having multiple copies of the data in play.
Decision-driven lessons from the 2020+ experience
One thing to be learned from recent experience is that there are two types of decision making needed for success. One, organizations anticipating a market influence and planning for it and being data-driven, will likely lead to a more successful outcome. These organizations will be more likely to survive. And two, organizations that anticipate unforeseen circumstances and are ready to pivot to new business models quickly. These organizations will not only survive but thrive. They may even be considered disruptors who worked quickly to take advantage of new opportunities. Being able to do both is often the hallmark of data-centric organizations, placing data at its center and treating it as a dynamic asset, one to be redeployed into new scenarios as opportunity and circumstances occur.
Striving to be a data-driven organization ensures your business plan and decision-making is rooted in supporting information. Becoming data-centric builds a long-lasting culture of data literacy, enabling all areas to understand the value of data, react to it, and grow with it exponentially.
Glyn Bowden is a CTO for HPE Pointnext Services, AI & Data Science Practice. His technical experience has spanned many industries from global finance, national security and high technology. With a background in high performance compute, cloud native computing and emerging technologies such as blockchain and machine learning, Glyn’s goal is to make high technology solutions accessible to all. He is an active speaker at industry events around the world.