Why Underuse Of Analytics Could Be Costing Organisations Millions?
Your data is an asset, but are you actively investing and realising returns or is it languishing unused in a fixed term deposit?
Most of us know the potential for competitive advantage stored within our CRM, social channels, customer survey results, financial systems and IoT logs, but struggle to realise the value for a variety of reasons.
“Forrester reports that between 60% and 73% of all data within an enterprise goes unused for analytics… despite the fact that more companies are talking about big data, using technology to capture more data ”
Equipment giant Caterpillar recently estimated its dealers were losing $9 – 18 billion in easy sales revenue by not monetising their data – ignoring real time customer stats, not providing a consistent customer experience and failing to communicate with each other.
Caterpillar had the systems in place, but identified a knowledge and training gap preventing its use. Most organisations are still a step behind, battling data silos, synchronisation issues, format variances, skill shortages and security concerns to present an integrated view of industry, customer or production data to support real time decisions.
The opportunities for leveraging analytics effectively are practically endless, but some examples that contribute directly to the bottom line are:
- Better understanding customers and trigger events for improved customer service and up-sell, approaching them at the right time with the right offer.
- Making pricing decisions based on predictive modelling around competitive offerings, stock levels and production capacity.
- Leveraging data from one area such as PPC campaigns to optimise others like product design and local area marketing.
- Fraud detection – immediate identification of anomalies and suspicious activity.
- Prospect identification – analyse characteristics of best customers and target lookalikes as well as avoiding any identifiable group that cost more than they deliver: late payers, frequent complainers etc.
“The biggest trend I see in enterprise AI in 2019 is machine learning… beginning to help companies exploit the vast amount of data they’ve been collecting and storing for years.” – Dean Teffer, principal scientist for machine learning at JASK.
A comprehensive data analytics strategy no longer requires a room of highly trained data experts and is more accessible than ever before. While a certain level of data literacy is essential for staff to interpret and use business information, advances in artificial intelligence have provided the answer to many organisations previously feeling stranded in a sea of unused data.
Data preparation
The beauty of many AI tools is the ability to understand unstructured data. Rather than requiring a neat spreadsheet of sales results, standardised survey scores and customer demographics, AI text analytics can “read” invoices, emails, social media interactions and other messy data sources, bringing structure and clarity.
Helping the humans
“Graphical and search-based interfaces to analytical programs are increasingly making it possible for people to find data and specify the analytics they need. ”
AI is very good at those painful, detailed tasks that support data analysis but no one likes doing: aggregating huge data sources, recognising patterns, identifying relationships and predicting outcomes. Graphical and search-based interfaces to analytical programs democratise analytics by empowering those with limited analytical skills.
Low impact intervention
Introducing more profitable BI into people’s days does not always have to involve new, shiny tools. Microsoft 365 business apps are a practical way to align the technology you already have such as Microsoft PowerApps, Forms, Flow, Power BI and SharePoint custom lists. Business apps require no or low-code knowledge to build and can incorporate these existing tools with the processes you need, capturing data across different sources and presenting a single view.
Alternatively, consider a bot providing suggestions and recommendations during existing workflows. A staff member asking a question on Yammer might be guided by a bot to a relevant sales report, or a customer consultant in the middle of a call may be alerted to a gap in insurance coverage.
Too many organisations are sitting on valuable data and missing out on its potential for competitive advantage. Whether your main focus is to enhance sales, minimise risk, or simply provide a better employee experience, there is huge potential in data analytics powered by AI and it’s getting cheaper, easier and more user-friendly every day.
Originally posted by Marcus Dervin.
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