Becoming a data-driven business doesn't happen overnight. It requires focus and clarity, and there are no shortcuts. The important thing is to start the journey and leverage the experiences into the next data intelligence initiative, while your organization matures along the way.
The data-driven company
Being a data-driven company, business decisions are made on the foundation of data intelligence and interpretation of those.
The starting point for being a data-driven company is the initiation of digitalization of data and the capability to apply business intelligence tools. For most companies, this has been ongoing for the past 15 years, focusing on IT technologies making data more accessible and not least presenting data in optimal ways. The focus is on reporting, preferably presented in dashboards, and most often targeting specific functions and audiences. Not least the top management.
With the definition of ‘data-driven’, all companies are in principle data-driven, so what's the point? It must be considered if being data-driven has become a strategic decision, and to what extent a company implements data-driven activities in practice.
Performing business decisions on the foundation of data intelligence and interpretation of this, demands significant different competences, when focus is inwards on business operations, compared to when focus is outwards, centering customers and business opportunities. The more customer-centric you become, the harder is the assignment.
For those reasons, the best way forward to become a truly data-driven company is by start simple, leverage the experience from the first initiative into the next and take on bit more advanced data intelligence initiative every time.
The extent to which a company uses algorithms instead of individuals to establish the decision basis and execute decisions, the more data-driven the company is.
Getting the information, you can act on is key. Developing predictions based on data for employees to evaluate and act on, is an effective contribution to performing daily operations. The employee doesn’t derive the conclusion from BI-reports but are presented with the predictions; the message of what should be done. As the applied data is more extensive and in real-time, the insights are improved and a better foundation for business decisions. As an example, is the prediction of prices of commodities, bunker oil, exchange rate etc.
The employee doesn’t make the decision, but act on the predicted result.
Removing your pain by automating annoying and time consuming, but still relevant tasks, frees up capacity. This first of all address the possibility of applying Robotic Process Automation (RPA) in repetitive and well-defined tasks. However, by being more ambitious, and leveraging advanced prediction, this can outperform human involvement. As the big amount of different type of data taken into account and the speed of which decisions can be predicted, it becomes faster than the human brain. As an example, is the prediction of selection of test samples in the public sector (purpose to detect fraud) and research (purpose of quality control in e.g. pharmacy).
In this scenario, the employee doesn’t make the decision nor perform the task. However, instead there will be a task to perform control of ‘close call-decisions’ and monitoring the prediction’s performance (predictions stays within decided confidence interval).
Making your customer happier by improving ways of working, which benefit your customers. You change processes, improve digitalization of the processes, to make it easier for your customer to perform, what you expect of them. There are low hanging fruits to be harvested. Nevertheless, it seems that companies deprioritize this. Not because the data intelligence element is harder, but it requires more to execute the initiative. It’s not just about automation.
It’s about re-designing ways of working and taking a customer-centric starting point. This requires competence involvement from across the value chain, something which takes high organizational maturity. There are many opportunities in this area, and it’s mostly grounded in the way companies interact with their customers, i.e. how information is exchanged. As an example, this can be collecting sample tests and report result or personalize website communication and workflow with individual customers.
In this scenario, employees need to learn new ways to work, other steps to perform, and very important; customers must appreciate the change, for the initiative to succeed. Otherwise automating and leveraging predictions bring no value for either party.
Giving your customers an edge by contributing to producing new products or services, which benefits your customers – and perhaps your customer’s customers are founded in the ultimate set of data and predictions. Collecting critical data, developing advanced algorithms which brings unique insights are valuable to customers and it comes in many shapes depending on type of business and industry. One example is a software program incorporated in a wind turbine control system, which collects information and predicts wind production, which is developed by the IT provider of the control system, purchased by the wind mill producer and benefiting the windmill farm owners and energy producer, as knowing the amount of wind energy production making them able to balance energy demand with conventional energy production sources.
For the IT provider to improve their product, information from their customer’s customers is required, to truly develop customer-centric solutions. An initiative like this would often be categorized as strategic and requires involvement from all parties in the extended value chain, and it increases project risks due to the uncertainty of succeeding. Not necessarily due to the complexity of developing advanced algorithms, but due to the amount of involved parties.
For this scenario, it requires involvement of all levels in the organization of several companies. There needs to be a profound understanding of how to develop new products based on data and derive predictions with the aim of creating new and improved products which creates the competitive edge.
There are two primary elements determining the company’s maturity level in data intelligence. First, how ambitious you want to be in the use of advanced data itelligence. Secondly, and equally important, how mature is your company as an organization, in performing advanced data intelligence initiatives. This embed how well the organization performs and support data intelligence product development, and how well the organization is to train and establish employee readiness.
For companies, it’s not about being the most data-driven company on the market; it’s about being the most efficient in maturing to become data-driven and bring return on investments on the data itelligence initiatives.
ABOUT Rooftop analytics
Rooftop Analytics can be your data intelligence partner in the task of developing your data-driven company. We know what it takes to work with advanced data intelligence initiatives and understand what it requires of the organizations to mature cost-effectively and with all employees on onboard.
The right framework for a data intelligence initiative is needed for it to succeed in creating data intelligence.
Bringing data into play opens for new business opportunities – also for small and midsize companies.