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Data collation has become perhaps one of the most important factors in delivering the levels of standards that today’s empowered customers demand. However, many companies are now finding themselves struggling to turn the vast amounts of data being generated today into meaningful insight. Bill Pollock, President for Strategies for GrowthSM explores how field service companies can find a balance between flood and thirst...

Many reports have been written about services organisations (and businesses of all types) “drowning in data lakes”. However, the key to success is to establish early on what data is needed to effectively run services operations, and focus specifically on those types of data when collecting and processing the reams and reams of data points generated from your IoT-based systems.

Too much data is … well, too much data, if you don’t have a plan to harvest it effectively.Too much data is … well, too much data, if you don’t have a plan to harvest it effectively.

The most important asset a services organisation owns is the cumulative knowledge and expertise it has acquired, developed and utilised over time to support its customers’ systems and equipment – and, in many cases, the entire customer enterprise.

Some of this knowledge may be in the form of bits of information stored in a database, while other knowledge is often manifested in the form of new systems, tools and technologies that have been placed into use.

However, knowledge can only be built on a strong foundation of data and information – and these key components of knowledge must be inherently accurate, clean, well-defined, and easily accessible.

Today, everybody talks about data analytics, but many often confuse data with information and knowledge. Basically, data is a core corporate asset that must be synthesised into information before it can serve as the basis for knowledge within the organisation. As such, data may be defined primarily as:

• Facts about things, organised for analysis, or used to support or make decisions; or
• Raw material from which information is derived to serve as the basis for making intelligent decisions.

Information, on the other hand, is defined as:

• Collections, or aggregations, of usable facts or data;
• Processed, stored, or transmitted data; or
• Data in context, accompanied by precise definition and clear presentation.

Finally, knowledge may be defined as:

• Specific information about something; that is, the sum or range of what has been discovered or learned;
• Information known, and presented in the proper context; or
• The value added to information by people who have the experience and/or acumen to understand its business potential.

The quality of the data that an organisation collects, measures and distributes is also a key factor in database building. To attain an acceptable level of data quality, the organisation must be able to mine its data whilst focusing on key areas, such as:

• Clear definition or meaning
• Correct values
• Understandable presentation format
• Usefulness in supporting targeted business processes

However, regardless of the state of the organisation’s data assets, there must still be a balance of data, process and systems in order to meet the company’s stated business objectives, which generally focus on things like:

• Increasing revenues and margins
• Increasing market share
• Increasing customer satisfaction

However, if there is not a match between data quality and the application of that data, then the entire process will ultimately become a fool’s errand (i.e., garbage-in; garbage-out).

Regardless, data is ubiquitous – it is used to support every aspect of the business, and is an integral component of every key business process. But, the usefulness of the data is only as good as the data itself – and this is where many organisations run into trouble.

You don’t go to work wearing 12 watches; you don’t buy 48 oz. of steaks, per person, to put on the grill for a summer barbecue; so, why would you pay for more data than you will ever needAs such, services organisations need to be able to identify which data is “need to know” vs. which data is only “nice-to-know”. Nice-to-know data is ultimately way too expensive to collect, process, analyse, monitor and distribute; however, need-to-know data is not only invaluable – but critical to ensuring the operational and financial well-being of the organisation.

For example, you don’t go to work wearing 12 watches; you don’t buy 48 oz. of steaks, per person, to put on the grill for a summer barbecue; so, why would you pay for more data than you will ever need when you can harvest just what you need for now (plus whatever else looks like you may need in the future)?

The quest for knowledge is the key that can unlock the potential applications and uses of the organisation’s existing – or planned – databases.

In fact, most businesses are already sitting on a goldmine of data that can – and should – be transformed into actionable information and knowledge with the potential to:

• Enhance and expand their existing product, service, supply chain, CRM and operational databases;
• Create knowledge-oriented delivery systems for new, or enhanced, value-added products, services and support; and
• Differentiate itself from other competitive market players.

Knowledge that was previously unknown – or unavailable – such as profiles of potential buyers, or specific patterns of product/service usage may be uncovered and put into practical use for the first time. The end result can lead to anything ranging from improvements in operational efficiency to improved service delivery performance, more accurate parts forecasting, and higher levels of customer service and support – all based on a strong foundation of data collection, measurement and distribution.

Consider your data repository as a storage space for all of the data you will need today, tomorrow and in the future. If large enough, put it in a data lake – but make sure you don’t use Loch Ness for what a smaller data lake can do for you more efficiently.

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Bill Pollock, President of Strategies for Growth explains why the future of the field service sector is going to be fully dependent on the emerging technologies that are driving ever greater connectivity...

Connected Field Service empowers Field Service Organizations (FSOs) with the ability to monitor equipment remotely, and transmit data into the business’ database in real-time.

The greatest manifestations of this increasingly pervasive technology may be best described in the following terms:

• Traditional Field Service Management (FSM) tools have long since taken their place in the everyday service operations of a large majority of FSOs
• Field technicians have been effectively repositioned as industry experts, equipped with data that helps improve productivity while delivering higher levels of customer service, and attaining enhanced levels of customer satisfaction
• Keeping up with the latest technology is an ongoing challenge for most FSOs – but one that is necessary to maintain their competitive position in an evolving competitive landscape
• Establishing a formal KPI program – with the flexibility to add new types of KPIs to address new ways of measuring connected field service performance – is becoming increasingly important.
• The more progressive companies have already begun to migrate toward newer, alternative business models, such as servitization or selling “power by the hour”.

As such, and by harnessing the power of the IoT combined with pervasive functionalities of a Cloud-based CRM platform, more and more FSOs have begun to shift away from the traditional “breakfix” repair model to a newer, “never-fail” service model. The combination of these new technologies with the adoption of alternative business models, are allowing companies to more effectively manage the entire business operations of the enterprise, rather than just its service operations – again, made possible through the advent and proliferation of connected field service.

Fast forward to today, we believe that the future of IoT-powered FSM solutions, particularly those built on a CRM platform, is quite bright.

Why? Because the value proposition is clear – and universal – across all segments and participants involved in the provider-customer services transaction:

• For services management – it provides a set of configurable tools, working in real time, that are necessary to make the critical decisions needed to run a successful services organization;
• For field service professionals – it provides immediate access to valuable data and information, and eliminates much of the cumbersome and repetitive paperwork required in the past;
• For the organization’s services customers – it provides the ability to initiate service requests and monitor call status directly via the Web (i.e., via a customer portal);
• For the parts/inventory organization – it sets the stage for controlled inventory and parts replenishment that helps keep costs down; and
• For the back office – it facilitates the streamlined flow of information between and among dispatch, finance, purchasing, parts/ inventory and all other relevant stakeholders within the organization.

The staggering amount of data that can be generated through a connected field service environment also brings to the table several new data-related capabilities for FSOs, including the ability to:

1. Collect whatever data that is needed to improve a process, or improve a product, based on its measured, monitored and tracked usage
2. Switch to a lower-cost predictive model vs. the more traditional – and more expensive – preventive maintenance model
3. Determine which services to offer to customers that the organization cannot offer today (e.g., a next-level guarantee against downtime, which can be turned into a premium service, etc.)
4. Sell, cross-sell and up-sell new services, packaged as competitive differentiators
5. Create a more effective KPI program that can measure, monitor and track both the still relevant traditional KPIs, as well as the “new” KPIs that are being created using connected field service

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 By 2018, half of business ethics violations will occur through improper use of Big Data Analytics, says Gartner.

Analytics projects that utilize big data or advanced analytics are increasingly popular but present a heightened risk of failure, according to Gartner, Inc. which says analytics leaders can improve the likelihood of success by following five best practices.

"Although big data and advanced analytics projects risk many of the same pitfalls as traditional projects, in most cases, these risks are accentuated due to the volume and variety of data, or the sophistication of advanced analytics capabilities," said Alexander Linden, research director at Gartner who is  presenting on advanced analytics at Gartner's  Business Intelligence & Analytics Summit 2015, this week in Munich, Germany. "Most pitfalls will not result in an obvious technical or analytic failure. Rather they will result in a failure to deliver business value."

Failure to properly understand and mitigate the risks can have a number of unintended and highly impactful consequences. Those can include loss of reputation, limitations in business operations, losing out to competitors, inefficient or wasted use of resources, and even legal sanctions.

Gartner also predicts that, by 2018, 50% of business ethics violations will occur through improper use of big data analytics. Following key best practices will help analytics leaders to improve the likelihood of success, and they include:

1. Linking Analytics to Business Outcomes through Benefits Mapping
   Analytics must enable a business decision maker to take action, and that action should have a measurable effect — whether the effect is directly or indirectly achieved. Linking analytic outputs to traceable outcomes using a formal benefits-management and mapping process can help the analytics team navigate the complexities of the business environment, and keep analytic efforts both relevant and justifiable
2. Investing in advanced analytics with caution
   Many organizations believe that Big Data automatically requires advanced analytics. However, the data-crunching power required to manage the big data characteristics of volume, velocity and variety does not inherently require any more sophisticated algorithmic processing. It is the complexity of the analytical question to be addressed that drives the need for advanced analytic tools, and in many cases desired outcomes can be achieved without resorting to more sophisticated analysis.
3. Balancing analytic insight with the ability of the organisation to make use of the analysis
   Because analytics can only be beneficial in organizations that are willing to embrace change, it makes sense to limit investment in analytics to a level that matches the organization's ability to use the resulting insights. Analytics may not be the most suitable approach if  pertinent data is absent, when there are high levels of ambiguity, or where there are entrenched opposing points of view.In these cases, scenario planning, options-based strategies, and critical thinking should also be incorporated into analytical approaches to better support the organization's ability to take action.
4. Prioritizing incremental improvements over business transformation
   Using big data and advanced analytics to improve existing analyses, or to incrementally update and extend an existing business process, is easier than using them to deliver business transformation, because there are fewer dependencies to overcome to ensure success. Care should be taken to validate the level of overall change required. In some cases, deep reform of the business strategy may still be necessary — for instance, when a new disruptive vendor enters a market, when technology innovation changes the business model, or when an organization has become dysfunctional.
5. Considering alternative approaches to reaching the same goal
   Few objectives can only be achieved in one way. Statistical modelling, data mining and machine learning algorithms all provide means of testing ideas and refining solution propositions. Big data and advanced analytics help validate proposed hypotheses and open an even wider range of potential approaches to addressing corporate priorities. Not all problems even require a fully engineered analytical solution. Investment may be better targeted on human factors, re-education or reframing the problem.

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A recently published study from Aberdeen Group commissioned by Trimble FSM has found that the best-performing field service organisations are extremely focused on improving service, and to achieve that, they are leveraging performance analytics to launch new initiatives and enhance existing ones. As a result, they are reaching higher levels of customer satisfaction and loyalty.

The report, Secrets to Optimize Field Service for Better Customer Experience, written by Aberdeen analyst Aly Pinder, revealed that top performers exceed customer expectations and SLA goals in their efforts to retain valuable customers, and that customer satisfaction is a leading contributor to their success.

Streamlining service in the field and improving efficiency are key objectives of today's executives, the report stated. Customer experience is a top priority, and organisations are leveraging analytics to drive quality and enhance customer interactions. More than 50 per cent of the organisations surveyed say they use performance data to evaluate the effectiveness of their service.

The field service organisation traditionally has been evaluated based on operational metrics such as workforce utilisation and overtime costs.

This model worked best when field service pursued a break/fix strategy but is no longer the only path to service differentiation and success, the report found. Customer experience must now be at the centre of the entire service operation's strategy.

Organisations meeting 80 per cent of their customer service requirements for issue resolution times on average are able to retain 12 per cent more customers than those that meet only half of their customer requirements, the report says. With service a key factor in customer loyalty and a leading indicator of field service success, it is critical that companies deliver on what they've promised, when they've promised it. For businesses with mobile workers in the field, it becomes even more important to achieve excellence in delivering services

 The report is available here (registration required)