Is Data in Business a gold-mine or a mine-field?

Posted on Thursday, October 24, 2019
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By Jonathan David
Services Portfolio Insights Manager

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Data Driven Success - How to use your data from start to finish

Every organisation uses data, and our Data Driven Success blog series delves into the challenges facing organisations around how data is used. Our expert Asset Intelligence Data Analyst, Dr Jon David, looks at the complete data ‘journey’, covering a range of topics to help organisations understand how to unlock the power behind their data.

Changing our viewpoint on data

Data is everywhere. Every day you make thousands of decisions that result from either a conscious or sub-conscious analysis of data. When do we cross the road? What do we need to buy from the supermarket? How do we approach a challenging conversation with a colleague? When you start to think of data in this way it becomes apparent just how pervasive data is. It isn’t just numbers in a spreadsheet, it impacts our lives completely - it’s everywhere.

How do we process this data? Continuing with the example above, our data inputs are our five senses and our storage medium is our brains. We apply our own logic, emotions, knowledge and experience to process these data inputs and initiate a response to bring about a desired outcome. Sometimes this is achieved and other times, not so much. Either way, we hopefully learn from this decision process and modify our future decisions accordingly.

So, how does this understanding of data relate to unlocking the potential of data in business? Well, there are several helpful parallels that outline what role data can play in driving business intelligence and the common sticking points in this process. The aim of this blog mini-series is to unpick this process and outline key considerations that organisations must address to unlock the potential of their data.

The First Challenge: How do we access our data? 

Our brains are very good at making connections between disparate data sources. For example, we can match faces with names, two very different types of data. Conversely, organisations often struggle to form these links due to the siloed nature of data across systems, platforms and languages. Existing IT systems frequently do not consider data as the central currency, with little emphasis placed on its potential future use and access. Strong data governance is required that stresses the transactional nature of data at the centre of the strategy. Organisations are far more likely and capable to dig into their data when they have a centralised, clean and fast data source.

The Second Challenge: How do we ensure data quality?

A misread sign could send us to the completely wrong place or a gap in a doctor’s knowledge could lead to an incorrect diagnosis. Similarly, organisations with incomplete or insufficiently validated data are liable to make poor decisions or no decision at all. It is estimated by Gartner that poor data quality costs organisations an average of nearly £12 million per year in losses. Furthermore, the New York Times estimated that data analysts must spend most of their time collecting and preparing messy data before its true value can be explored. Organisations must prioritise establishing a single source-of-truth that emphasises validation to generate clean, reliable data for analysis.

The Third Challenge: The power of context, turning data into information

Data without context is just data. Data with context is information. Information can drive change. How our brains interpret data depends heavily on this context, such as the tense of the word ‘read’ in a sentence or the meaning of a cultural practice. It is only once we are armed with this information that we can begin to make informed decisions. From a business perspective, the context surrounding data is key in selecting the most appropriate analysis and generating accurate results and predictions. For example, a café selling ice creams may forecast greater sales and improved profits in summer due to warmer weather. Whilst a reasonable assumption, this café is actually located on a University campus and so most of its client base are elsewhere during the summer. By contextualising data in this way, unnecessary wastage of stock and maximised profits was avoided. Whilst a simplistic scenario, it highlights the need for organisations to prioritise the generation of actionable information through careful analysis of contextualised data.

The Final Challenge: How to present the right information to the right person

So how do we present this contextualised information effectively and relevantly to our intended audience? We wouldn’t explain how a car works in the same way to a child as we would to an adult or expect your average driver to understand the inner workings of a car like a mechanic. We are selective and intentional with how we communicate information and the same must be true in business. Consider the myriad of different roles within your organisation and how different data is applicable to different positions. For example, at the executive level, the development of relevant and proven Key Performance Indicators (KPIs) are critical to summarise the wealth of company data and benchmark against market trends. Ultimately, it is the role of a data analyst to present this data in a timely and relevant manner as intelligible information that can be used to drive insights across all these aspects of an organisation. Careful consideration must be given to the visualisation of this data, such as summary figures or graphs, to ensure that information is conveyed effectively and succinctly.

Summary: Driving intelligent change through data analysis

This blog has outlined in brief the four key challenges facing organisations aiming to successfully unlock the power of their data through intelligent analysis. The following blogs in this mini-series will tackle each challenge in greater depth, outlining important factors and exploring practical solutions that organisations should consider during this process. Data can be a catalyst for change, but only if organisations are willing to alter the way they view and value data. Is yours?

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