Where is the wisdom in the data you have? “It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.” Sir Arthur Conan Doyle.

The amount of data we produce every day is truly mind-boggling. There are 2.5 quintillion bytes of data created each day, and this is going to exponentially explode with the growth of the Internet of Things (IoT). I find it hard even to say the word quintillion, let alone recognise the scale. Over the last two years alone, 90 percent of the data in the world was generated.

If you look inside your own organisation, you will find masses and masses of data that you are accumulating. Whether it is near time event data as you monitor servers, network devices, applications, or other connected devices. Or customer tickets – reactive, proactive and requests. Through to web traffic, phone stats, etc. We are literally drowning in data. And all through our own creation.
We have followed, blindly in many cases, the adage that “what gets measured, gets done”. This means for many organisations, decisions have to be grounded in data. Now, whether that means the right data, and how you consume, present and reflect on that data is a challenge; hence the need to gain wisdom from your data.

Presenting at a service event a business case, I shared the detailed approach that helped an organisation correctly identify and then use the data that it had to make significant service improvements for a number of its customers. Let me explain.

Discovery Phase: First, Identify the reason for improvement

Before an improvement plan is executed it is necessary to understand the need for improvement. In this business case, a number of customers had been sold a very similar solution approximately eighteen months previously and it had been deployed out across the end-user estate. The incident call volume was 20% higher than predicted (how it was predicted was also a mystery!). there was anecdotal feedback from the end-users that the devices were being impacted by weather conditions (excessive cold for example) and the customer satisfaction was poor. A thorough understanding of business objectives and the services provided needed to be documented prior to any data driven service improvements. This also helps on the effectiveness of the improvement plan.

Discovery Phase: Next, State what will you measure?

It would have been easy to jump to conclusions and just look at the incident data to find out what would be easy areas to focus on. Say for instance lots of calls logged with connectivity issues, could have meant, looking at how the devices were connected to one another. Instead, look at all of the different data sets available to you. Event data; call data; incident ticket data; web-logged data; service and repair data were all captured related to these customers and also the products that were supported. Interesting as well, we used data from other customers with similar products to do comparisons to see if it was customer specific, product specific or not.

Discovery Phase: Collect the Data

Data was gathered according to the goals and objectives of service operation. The quality of data is critical, as the saying goes “garbage in, garbage out”. Even something as simple as a customers postal address can be different in different data sets – with or without a post code for example. When you are collecting and ingesting massive amounts of data, it is useful to use a presentation software solution, that enables you to quickly identify outliers, anomalies in the data and areas that are missing.

Discovery Phase: Process the data and turn it into information

Once the data is collected, it is important to provide it to the audience in a format where the joined data sets actually make sense. For example, use a hierarchy such as customer, geographic area, town, location. Or Service, then product family, finally, product. The raw data is organized and divided according to these categories and service operations which makes it easy to process and transform the data into information.

Discovery Phase: Analyse the information presented and turn it into knowledge

The data which is linked together in the data analytics solution is now effectively a series of information capsules. This information is carefully analysed to find missing gaps and its impact on the business, the service delivery, and the repair of the products. The information is thoroughly evaluated taking into consideration all relevant – internal and external factors that can directly or indirectly impact the outcomes.

On top of the normal data we had to hand, we also ingested weather data, based on geographic locations around the United Kingdom, to see if there was a correlation between failures and weather. We added in software release dates; repairer service updates and customer driven changes to ensure the widest possible analysis. We added public holidays and also key local events to see if some of the issues were related to customer volume variations. The information was effectively converted into knowledgeable facts.

Control Phase: Present and use the knowledge

The analysed information was presented live through the presentation software solution and shared with the business stakeholders in a clear and defined manner, presenting them an accurate picture of the results of the analysis and sharing with them the proposed improvement plan that is going to be implemented.

Control Phase: Implement corrective, test pilots or remedial activities

Twenty-four recommendations were identified across either the end-user customers, service desk, field service and repair areas. These were either process changes; service delivery changes; usage changes; or physical design changes to the service and product.

With twenty four changes that need to be made across fifteen customers, eight hundred plus locations and nineteen thousand devices, it would have taken years to enact that level of change. That is where the you move to the final stage…

Finally, Predict Phase

Where you continue to develop wisdom With live, daily monitoring, presentation and checkpoint change tracking in the presentation software solution, we were able to make rapid parallel changes that within six months had driven the incident volume down, improved reliability and improved customer satisfaction.

The methodology, tools set and presentation software solution have been rolled out across a vast number of customer estates and is supporting a structured approach to service management. I leave you with the following quote which makes me smile...

"Where is the Wisdom, we have lost in Knowledge? Where is the Knowledge, we have lost in Information?" T. S. Eliot (1934)

Writer References to data numbers:
1. https://blazon.online/data-marketing/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/
2. https://goleansixsigma.com/dmaic-five-basic-phases-of-lean-six-sigma/ - DMAIC phases of Lean Six-Sigma
3. https://www.getapp.com/business-intelligence-analytics-software/ - Business Intelligence analytics platforms

Features future of field service Martin Summerhayes data analysis Data Management

About the Author: Martin Summerhayes

With a passion for achieving outstanding levels of service for customers and a wealth of experience in customer experience led strategic and operational business change initiatives; service improvements and full product and service lifecycles, Martin is able to offer significant value to organisations that want to deliver exceptional customer success services.

Where is the wisdom in the data you have?

Discovery Phase: First, Identify the reason for improvement

Discovery Phase: Next, State what will you measure?

Discovery Phase: Collect the Data

Discovery Phase: Process the data and turn it into information

Discovery Phase: Analyse the information presented and turn it into knowledge

Control Phase: Present and use the knowledge

Control Phase: Implement corrective, test pilots or remedial activities

Finally, Predict Phase

About the Author: Martin Summerhayes

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Where is the wisdom in the data you have?

Discovery Phase: First, Identify the reason for improvement

Discovery Phase: Next, State what will you measure?

Discovery Phase: Collect the Data

Discovery Phase: Process the data and turn it into information

Discovery Phase: Analyse the information presented and turn it into knowledge

Control Phase: Present and use the knowledge

Control Phase: Implement corrective, test pilots or remedial activities

Finally, Predict Phase

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About the Author: Martin Summerhayes

Leave a Reply

Follow Field Service News

Latest from Twitter

From The Archives

Kirona Launch Workflow Management Solution

Siclops advances to the cloud

Aquant Launches New AI Tool, Service Insights