A look at Data Driven Work Management - Part 1
This blog post is part of a series of posts extracted from our “Data Driven Work Management” Whitepaper. Read the full paper at https://insiris.com.
Work Management (“WM”) businesses (and by this, we mean any organisation that employs teams that carry out work away from a sole ‘back office’) face a unique set of challenges. The disconnected nature and mode of operation of a scattered workforce (often geographically) brings distinct problems — and opportunities. Extreme competition over the past decade and the increasing democratisation of service, coupled with an increasingly more data-savvy customer base has forced businesses to radically, and in some cases, fundamentally, change their modus operandi.
A recent survey of more than 500 senior executives, indicates that companies making decisions based on data tended to do better financially.
Data is the backbone of any modern WM organisation. Without it, companies can languish, blind to the history of what happened, perilously oblivious to what is next around the corner. Becoming Data Driven is one of the most important, transformative steps a business can take.
Being Data Driven means agreeing with a fundamental principle: that the data a business generates, manages and collects has intrinsic value that, with the correct framework, can be put to work. Many businesses believe that their value lies in the product or service they offer — their unique selling point, or ‘secret sauce’. A Data Driven organisation recognises that not only is their service unique, but the data that orbits their operation is just as unique, and just as valuable.
Data Driven businesses understand that data needs to be part of the company culture from the top down and the bottom up, not the exclusive remit of specific ‘Data People’. This widespread deployment of data to all employees — the ‘democratisation of data’ — is a key tenet of the philosophy. Conceptually, the principle is agreeable, but deploying a strategy of data democracy — equipping all employees with access to data — can be a huge undertaking. An undertaking that can reap massive benefits: MassMutual, a Life Insurance Company, undertook a data democratising program to equip workers with access to data through new systems and a company wide App, leading to improved close rates on employer-sponsored retirement plans which grew from a 50% long term average to over 80% .
Organisations are beginning to understand the value in data through the media buzz in recent years around ‘Big Data’. New reports indicate that ‘small data’, however, can be just as useful — and transformational.
There is little correlation between financial performance and a company’s definition of ‘big data’ the number of datasets that a company uses, or the number of employees whose job responsibilities include analysing data”
59% of respondents in a recent survey say that the data their organizations hold is becoming “a core component of their market value”. WM products and services are particularly customer centric — and low margin levels make for an ultra competitive sector. Understanding the customer, their actions, their requirements and their view of you, the supplier, requires a data centric approach. Understanding the value of the data an organisation has, and building a service offering that puts the data facts at the centre, is key.
Companies may understand the ‘why’ of data, but the ‘how’ of putting these principles into practice can remain elusive. Only 27% of executives in a recent survey described their data initiatives as successful. This demonstrates just how hard it can be to enact a Data Driven approach in reality.
Established and incumbent WM businesses are realising that data silos and a failure to centralise and extract data value is making innovation difficult. This in turn is leading to threats from competitors and start-ups.
The organisations that are succeeding are those that are successfully implementing three key transformations:
Gartner predicts that by 2020, “10% of emergency service work will be both triaged and scheduled by artificial intelligence”, and that by 2022 “one- third of complex field service organizations will utilize machine learning to predict work duration and/or parts requirements”. The single biggest inhibitor to the deployment of AI powered technologies is lack of quality, timely data. The Data Science Hierarchy of Needs outlines the need to gather the right data, in the right formats and systems, and in the right quantity. Any application of AI and ML will only be as good as the quality of data collected.
Professor Gary Marcus, of New York University (and former Head of AI at Uber) concludes that a “General Purpose AI” — one that can address any problem — is problematic and not attainable in the near future. In the meantime therefore, AI and Deep Learning are techniques that must be applied to businesses in specific, targeted ways. Even generalised AI in areas such as “Dynamic Scheduling” require a Data Driven focus, and complete understanding of the data captured; it’s the reason why one of the only consistently well performing scheduling software functions is Route Optimisation — a problem that is singularly well understood.Businesses have had mixed results with the deployment of automated scheduling technology, where project failure relates to complexity of operational processes.
The second critical inhibitor to the deployment of intelligent, AI driven solutions, is the lack of skills in this arena — and relatively high level of specialism required to understand the design and application of the technology. The August 2018 LinkedIn Workforce Report found that there were more than 151,000 data scientist jobs going unfilled across the U.S. In terms of skills shortages, big data and analytics is the number one place of need, according to a recent survey with 46% of CIOs who participated in the survey said they suffered from a skilled shortage, followed by a shortage in AI skills at 38%1. Organisations are increasingly turning to tech providers to fill data science and AI gaps in their Data Driven strategy.
Part 3 of our Data Driven Work Management series
Part 2 from our series of posts extracted from our “Data Driven Work Management” Whitepaper.