Defining your business strategy is a game with many moving pieces – internally as well as externally, locally and globally. The glue holding these pieces together is information, it’s data. To see the best strategic options, it is important that your decisions are supported by qualified, analysed and appropriate data.
The use of big data and supporting analysis already now forms part of the top corporate agenda. Data and analytics transform the way companies act, interact and do business. This calls for discipline in the development of data-driven strategies, making them your competitive edge and a differentiator.
Some key elements in using data to support your strategy:
- Choose the right data
- Create ways to support the prediction of business outcomes
- Enable your organization
Choosing the right data
We face a vast and ever increasing number of data sources. Both volume and granularity are rapidly growing. As is our ability to capture it. With more data companies gain the ability to see new areas and layers of their customer experience, support their operations, and feed into their strategy.
Up front this creates a demand for a proactive and critical view of available data sources. Often the data we need is already available, but we may not know where to harvest it.
Analysing potential data sources and effectively retrieving the right data, filtered or simply put into a new context, often provides new insights.
An explorative mindset is very much needed when working with big amounts of data. In an increasing number of organizations, we see a designated Data Officer role that can grasp this minefield of opportunities and challenges in data management. The Data Office asks the right questions to identify the metrics for a right decision.
Create ways to support the prediction of business outcomes
When you establish the first attempts of filtering and analysing data in order to answer strategic questions, have a pragmatic approach towards the process. Find the least complicated way to get useful data or the least complicated way to improve your current behaviour.
The competitive advantage lies in the ability to predict and optimize outcomes, supported by the right analytical models. There is a real danger in spending tons of resources on analysing data before you have identified the business opportunity that the model and data should be applied to.
The right order of activities:
- Identify a business opportunity
- Determine a model that supports the idea
- Identify the required data
- Gradually advance the algorithms
The object is to automate data handling processes, filter and enhance, add an intelligent layer and enable us to make reasonable predictions, forecasts, trend analysis and support qualified decisions. The automation part is a must when addressing big data. The enormous amounts of data are of no real value to humans, if not processed intelligently by powerful servers and presented in digestible formats.
The Infrastructure challenges
When you start looking into your data structure and sources, you may find old legacy systems, isolated data, cloud-based data, and possibly an IT architecture that prevents you from harvesting and filtering of data in a smart way.
If your starting point is to collect all available data, these challenges will prevent getting the optimal results. Identify the data you need, in order to answer your strategic questions, is the first step you need to take in order to reap the benefits.
Trending technologies such as Robotic Process Automation (RPA), Artificial Intelligence (AI), and Machine Learning (ML) could easily be part of your strategic direction. Here, the success rate relies largely on a consistent flow of quality data. For Robotic Process Automation data is needed to verify the outcome of the process, while both Artificial Intelligence and Machine Learning need data to improve the cognitive ability of the algorithms and to ensure scalability.
Enable your organization to benefit from data
With new models and data tools available, you need to enable your organization to generate value from the new analytical procedures and the generated outcome. In one survey, 77% stated that “business adoption” of data and AI initiatives continues to represent a challenge for their organizations (New Vantage Partners). Of the respondents 95% contributed the struggles to cultural and people issues, rather than technology (5%). This emphasizes the need for a people-oriented approach to the implementation of Data-Driven Decision Making.
For many Data-Driven Decision Making can be a new and different approach to identifying opportunities and solving problems, based on analytical results of data as opposed to traditional and more reactional behaviour.
Whenever a change of tools, methods, behaviour, or culture is introduced, the need for education and Organizational Change Management (OCM) is present. You can read more about OCM in the article by fellow Sofigator, René Bomholt, “Change Management: the art of moving from “why” to “what”.
Even simple changes such as new data-analytical procedures and perhaps new tools to support the analysis will require real attention to the human part of the organization.
If you would like to learn more about data-driven strategic approaches, feel free to contact the author at email@example.com.
New Vantage Partners. Big Data and AI Executive Survey 2019. Online resource.