How To Squeeze Hidden Value From The Hidden Data You Didn’t Know You Had

How To Squeeze Hidden Value From The Hidden Data You Didn’t Know You Had

James Crossland

Digital Marketing Manager|Kerv digital

Published 06/07/22 under:

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Implementing Effective Data Discovery Processes

 

Data… it’s become such an ubiquitous word hasn’t it?

Organisations are bombarded everyday with data on their clients, data on their customers, data on their competitors, data on their donors, data on their members and/or data on their supporters. That’s a lot of data and sometimes it’s hard to know what to do with it.

Even worse, sometimes it’s hard to even collate and categorise it or… worst of all, realise your organisation has it in the first place.

 

That’s not good.

It’s not good for your organisation. It’s not good for your staff and it’s definitely not good for your clients.

In fact, the only thing it is good for is your competitors!

 

In the modern world of cloud computing we now all find ourselves in, it’s vital to squeeze every last drop of wealth from the data your organisation has to hand and use it to derive useful and actionable business insights to maintain a corporate advantage.

 

In fact, several studies have found that nearly 80% of enterprise level executives believe that any organisation not currently adopting the use of Big Data will likely lose any kind of competitive hold on their respective market and could even go under when faced with competitors that are.

 

To ‘discover’ the data you didn’t know you had and then go on to derive useful business insights from it you’ll need to undertake a program known as smart data discovery, sometimes also called augmented intelligence.

 

What Is Smart Data Discovery?

At its core, smart data discovery describes a process through which an organisation can collect and collate data from wide ranging and disparate data sources and then apply it in such a fashion as to garner actionable business intelligence.

The end goal being for the organisation to benefit from an improved data driven decision making process, capable of sharing information efficiently, fluidly and instantly across all departments in a two-way process.

 

However…

Becoming a data driven organisation means first getting to grips with and understanding all the data that is available to you.

Data Discovery is a methodology that empowers everyone within an organisation to make the most of the data available to them by allowing them to derive insights from it in an interactive way.

 

But how does data discovery work? What tools do you need? What platform? How does it go from model to real world application?

 

How Does Data Discovery Work?

Smart data discovery ‘works’ by, as previously mentioned, collecting data from various sources and then looking for patterns within that data with the help of AI, ML and advanced analytics and visual navigation (usually Power BI), which allows for the consolidation of it all into one place.

It uses AI and ML (machine learning) to improve the business intelligence collected whilst automatically searching for hidden trends that can greatly increase the speed in which decisions can be reached.

 

Imagine you’re a CEO, CIO or CTO, running an organisation that needs a way to better view their data and derive in-depth value from it.

Information is key to any good organisational decision making process… for you or a competitor, which means anyone able to better analyse patterns and discern deeper trends automatically gains a competitive edge within their sector to allow them to better meet targets, ensure success and remain relevant.

 

As we already mentioned, data discovery isn’t a platform or a tool, it’s a process that needs to be bedded in and that process can be broken down into two major component steps.

 

  • Data Preparation
  • Advanced analytics

 

Augmented Data Preparation

Augmented data preparation can grant a CEO, CIO or CTO access to data that, simply put, is more ‘purposeful’. It allows all assumptions, strategies and approaches to be tested with ease, based on intelligent decision making protocols.

 

Augmented Analytics

Augmented analytics are a form of business intelligence that use machine learning algorithms as well as natural language processing that automate the insights you receive in from your data.

Taking the concept to it’s most basic form, it simplifies the process of arriving at actionable insights by automating data preparation as well as empowering data sharing across an organisation.

 

Why Is Big Data Discovery Becoming So Important All Of A Sudden?

Quite simply put… the amount of data created by the human race is growing at an exponential rate.

Studies have shown that more data has been generated in the last year or two than in in the previous entirety of human history and that since 2012 big data, data discovery and more recently, augmented intelligence has generated over 13 million jobs across the globe.

It’s popular for two reasons… first, no one person (or even group of people) can trawl through that much data for viable insights and secondly, giving an organisation the power to predict patterns and make connections that had never been imagined before offers a huge competitive edge.

 

That phrase ‘competitive edge’ can be quite nebulous though. In the specific, augmented data discovery allows for:

 

  • Tracking specific business performance indicators to specific metrics (KPIs)
  • More accurate predictions, and to grow from those, solutions, across all departments of an organisation.
  • More time is freed up for strategic decision making
  • Access to credible data becomes accessible for everyone within the organisation
  • Increased ROI and TCO (total cost of ownership).

 

However, with technology changing and advancing at such a rapid pace, new analytical techniques, process and resources are constantly being added.

 

How To ‘Do’ Augmented Data Discovery

 

  • Know What You Want To Achieve: The first step in any augmented data discovery project has to be to clearly define your organisations business goals. Doing so keeps the project focussed on collecting the right information required for the right goal. It’s important to seek input from all stakeholders and staff, not just those who will use the data, but those who’s responsibility it is to collect it as well.
  • Know Your Pain Points: Once you know what you want to achieve, it’s just as important to identify the obstacles that are going to stand in your way. No two organisations will ever have the exact same pain points but some of the most common Kerv Digital see a lot are; not being able to access the large amounts of information needed or having only a slow or limited access to it; struggling to collate a wealth of data from disparate sources; too much time is spent curating the data rather than understanding it and deriving useful business insights.
  • The More Data The Better: Data, unfortunately, doesn’t just come from one source (wouldn’t it be nice if it did though). Your customers will have multiple touchpoints with your organisation and all that data will appear in different formats, making it that much harder to collate into a useable format. To garner any kind of usable intelligence, as much of that data needs to be collected as possible but, and here’s the important part, transformed into a format for the ML to be able to derive insights from it. The data might be structured, it might be unstructured… the only way to derive new insights is to make all that data readable.
  • Your Data Needs Cleaning: Once you’ve started gathering all that data, you’ll need to hit a routine of cleaning it for it to be of any use. Some of it may be wrong, some of it might be missing fields, there may be duplicates or incorrectly formatted data still. You can go deeper still. If, like many organisations using augmented intelligence, you’re collating unstructured text data from sources like social media then extra care will need to be taken to clean it to avoid syntax misunderstandings, invalid characters or spelling errors. The ultimate aim of this ongoing cleaning is to avoid any data that runs the risk of generating misleading data that could potentially harm the business with incorrect intelligence.
  • Developing An Advanced Data Discovery Model: Developing an advanced data discovery model is the strategic approach to utilising an organisations data. As already mentioned, it will involve the collection, curation and analysis of said data as well as the data-driven decisions an organisation makes upon the discovery of new insights. Choosing a reporting tool to highlight this information will be a big factor in the success story. To help model that will require the aid of advanced diagrams, symbolic references and textual information used to represent how data is reaching, flowing through, and being used by an organisation.
  • Start To Tell a Story: All of the above might sound complicated (and it is and we absolutely recommend you seek technical know how to implement it efficiently) but the easiest way to make use of the data once you have it is to use it to tell stories. These should be easy to follow, that everyone in the organisation can understand and agree with, regardless of how technical they are (or more likely) aren’t. To help achieve that goal, data visualisation tools like Microsoft Power BI will be vital. Telling a story, or perhaps paining a picture with your data, ensures it’s accessibility and uptake throughout the organisation.
  • Automate The Process: Once all of the above has been planned out it’s vital to determine how the process can be automated as there’ll be far too much data to ever do this manually with any kind of success rate (or without occurring a ridiculous amount of human error).

 

In summary, a successful advanced data discovery (or augmented intelligence) program shouldn’t require anyone with advanced technical skills to understand the output (setting it up might be a bit different of course).

Anyone within the organisation should be able to derive actionable insights from the end product.

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