Lessons from the Data Whisperer
Article by Tania Armstrong | Published on March 6, 2017
One of the major challenges with BI in an organisation is the move beyond simply being a means to generate reports and move towards providing the ‘Intelligence’ that will drive the improved performance and insight that organisations are being promised from their BI solutions.
To achieve this requires an organisation embraces a ‘data culture’ approach that essentially treats data as a resource that can be used to generate value and business outcomes. This post outlines what we have identified as 7 key principles that apply.
But firstly what to we mean by data
When we talk about data - we are referring to the items of information that are captured by the operational systems of an organisation.These systems can be machine based (e.g. flow valves), structured software (e.g. ERPs), unstructured software (e.g. manually maintained spreadsheets) or paper based, essentially it’s the raw ingredient of the business’s myriad systems.
It is not only limited to internal systems but also those that are external to the organisation. As an aside the ease with which external data can now be accessed is one of the major shifts that has occurred in the data landscape in recent time.
On to the principles.
Principle One: Data is a raw ingredient
Very simplistically a manufacturer takes a raw material, such as iron ore, and applies knowledge, processes, people & technology to transform it into something of value. The same principles can be applied to data. The magic of raw data is that it can be both an output and an input to organisation. Processes within an organisation will generate data, such as the sale of a product to a customer generates a number of data items which will be captured in a point of sale system. By the same token data is also used as an input to processes within an organisation. An example of this could be the customer sales data being used for setting the budget for the next year. However context is key. Without context, benchmarking, further supporting trend analysis for example its value to stakeholders is limited.
Principle Two: Data is an asset
If the business views its data as a resource then it should be treated as an asset and provided the same care and attention as any other valuable business resource. This means that it should be stored safely and securely, that appropriate access is given to those that use it and that the quantity and quality is monitored and managed. The quality of the data resource impacts on what business benefit can be extracted and requires a master data governance approach (see our blog – The Red Queen, data deluge and the secret of MDG).
Principle Three: Context is key
Unleashing, harnessing and getting end user adoption from the potential of a business’s raw data is the ultimate BI success story and it starts with current state: How does the business use its current data? Getting in the head of your businesses users is essential to driving value out of what you currently have. If data is a raw material, a BI solution (whatever that looks like) must be able to provide its consumers the correct context in which to analyse the data. Without the context there is the very real risk of the wrong conclusion being drawn by the business. For example, using the number of orders as a proxy for sales without providing the context that this includes unfulfilled orders, or excludes late arriving orders, is asking for confusion and misunderstanding. Context is usually relatively straight forward when referring to data from internal systems but there can be significant challenges when using externally generated data either separately or in combination with internal data.
Principle Four: Not all information is created equal
The quality of the data resource impacts on what business benefit can be extracted, so it is critical that the business and key stakeholders be bold and ask questions about their underlying data until trust is established. Where does the data come from? Is it up-to-date? What data quality rigor is being applied? Is it fit for purpose or is the business collecting the wrong metrics? Most importantly does the BI process allow traceability? Can the system be integrated to determine where and if there is data validity issues?
Principle Five: Applying Business Knowledge to data creates Information
Once the data has context, is clean, concise and can be integrated then the application of business knowledge will turn that data into useful business information. This is the step where the implied ‘intelligence’ aspect of BI begins coming into play. Intelligent businesses ask the questions which BI solutions will seek to answer – it may be modelling up the future of agricultural exports should the Chinese market diminish, creating more customer relation ‘touch points’ in a transaction cycle in white ware retail / manufacturing or looking at bench marking across post-op orthopaedic healthcare patients - whatever the questions having a subject matter expert on hand with other stakeholders will help the BI support team develop the businesses future state and will ensure that the end solution will answer the questions the business seeks.
Principle Six: Businesses evolve as so must Information
Businesses evolve over time and so must information requirements also need to evolve. This does speak to the requirement for our BI solutions to be not just robust but also agile and able to be modified to meet changing business requirements. This requires an approach to both data and information that recognises that there will be a requirement to have different processes as information goes from initial discovery to business critical.
How we identify this evolution in business information is addressed by the 7th principle.
Principle Seven: The feedback loop
Like any system, the transformation of data to information needs to have feedback loops so that learnings, both good and bad, can be applied to improve the quality of the output. This is done by having the team that supports your BI solution integrated with the people in the business that consume the output and identify the business requirements. In addition, not only should your support team be integrated into the business but they must have an attitude of partnership that facilitates open communication with the business and develops understanding of the business challenges and how they can assist in addressing them.
Summing it up
One of the changes over recent time has been the ease with which organisations can now get access to data about their business. But, in my experience, it not sufficient to simply have access to data it is necessary to recognise and treat data and the subsequent information generated as an asset and a business resource. When this is done then an organisation is in a good position to be able to move beyond reporting to realising the ‘Intelligence’ in Business Intelligence.
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