Is Your Organisation Drowning in a Data Lake? Or Is There Not Enough Data to Drink?

May 3 • Features, Future of FIeld Service • 1414 Views • No Comments on Is Your Organisation Drowning in a Data Lake? Or Is There Not Enough Data to Drink?

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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 need

As 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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