Matthew Buskell, Area Vice President at Skillsoft, explains why people are as important as technology when it comes to a successful AI deployment
Today’s organisations are increasingly dependent on data-driven insights to retain and assure their competitive edge. Once the preserve of finance and tech industries, which historically produced a lot of data, today the ability to make informed business decisions based on data is a priority for companies in all industry sectors.
Thanks to the emergence of new technologies like machine learning, big data sets can now be analysed more effectively to improve operational performance. Indeed, organisations of every size are using data analytic techniques to build products and services that truly resonate with customers, to improve supply chain efficiencies, or engage in smarter marketing and improved customer management.
It’s a digital transformation that’s driving faster decision-making in just about every area of business, from ecommerce to product development, and helping to close the gap from insight to action.
The growing data imperative
Applying data to inform business decisions and measure outcomes is now key for creating organisational strategies, managing operations, and effectively governing the business. Little wonder then that the role of the data scientist has become pivotal. Demand for data specialists is booming. According to Indeed.com, job listings for data professional roles have grown by 256 percent since 2013, as reliance on data-driven insights to generate business values grows.
It’s a trend that’s only set to escalate. With consumer expectations on the up, organisations are now reshaping business models to compete in the experience economy. With companies like Amazon, Uber, and Netflix relying on the power of artificial intelligence (AI) to up their game, and using data to provide better, more streamlined experiences, leveraging AI just to stay relevant and avoid market disruption is becoming a business imperative for organisations everywhere.
Harnessing AI and machine learning to get — and stay – ahead of the curve
As the adoption of AI escalates, organisations are under pressure to apply advanced AI techniques to a variety of business functions and extract maximum value from the sheer quantity of big data that’s available to them. Determined to stay ahead of the curve, they need to respond faster, iterate more rapidly, and deliver optimised solutions that drive business impact and new customer behaviours.
Those companies that have successfully embedded analytics into their business are already reaping some impressive results. According to a recent MIT Technology Review report, 75 percent of organisations using AI have realised a 10 percent increase in customer satisfaction, while 34 percent of businesses saw an impressive one-third uplift in lifetime customer value.
Leveraging big data, AI and other technologies like the internet of things (IoT) to generate predictions and uncover insights that can be used to reduce costs and optimise business models, however, depends on having the right people and processes in place to drive swift business action.
That means capturing the right data, coordinating data better, and using specialists with the right technical ability and communication skills who can translate organisational goals into data-driven deliverables.
Yet 65 percent of CIOs report a lack of talent is holding their organisation back. Indeed, the battle for talent capable of unlocking the value of data is intensifying, with the US alone predicted to face a shortage of 250,000 data scientists by 2024.
Data roles continue to diversify and evolve
It’s not just data scientists that are in demand. As the field has grown, data science team roles are becoming more defined and differentiated with data analysts, wranglers, scientists and engineers, among others, needed to support the successful development and integration of technologies within an organisation.
With 80 percent of companies classifying their data as inaccessible, untrusted or unanalysed, organisations are quickly coming to the realisation that they need high performing cross-functional teams that include data architects and data visualisation experts.
Against a backdrop of talent scarcity, the supply-demand gap for data scientists and other data specialists is unlikely to close anytime soon. While data science graduate programmes are growing, and new entrants who have honed their skills via massive open online courses are coming onto the scene, organisations need to urgently reassess how they capture and grow the talent they need to progress their data analytics initiatives.
Tailoring the right employee value propositions
Some forward thinking companies are now reconsidering their employees as internal clients, rather than skill, knowledge or service providers. To attract and retain such highly sought after candidates, they’re formulating compelling employee value propositions (EVPs) that can be used as a powerful recruitment and retainment tool.
With 47 percent of generation Xers and millennials rating promotion or job advancement as one of the top three reasons to stay with an organisation, and 29 percent of generation Xers and 31 percent of millennials saying the same for development opportunities, offering considerable advancement and development opportunities represents a significantly persuasive element of any EVP.
Evaluation of job tenures for data scientist roles reveals employees typically stay less than 12 months, so keeping this cohort of top talent within organisational walls means companies need go beyond simply offering a job to providing clearly defined career pathways that are curated for specific individuals. Those employees that engage with the learning and development (L&D) opportunities on offer should then be rewarded with a clear way forward within the company.
Retaining and upskilling current employees
Advancing current employees with targeted learning paths is also critical to developing the talent that’s needed in-house. With data science team roles evolving and becoming more specialised, upskilling, pre-skilling or reskilling existing personnel ensures organisations can retain invaluable institutional knowledge and develop the unique skills they require.
Skillsoft’s Aspire Data Analyst to Data Scientist Journey is just one example of how companies can take aspiring data analysts on a learning journey composed of sequenced courses and multimodal content that enables them to progress to their next role. Along the way they develop the ability to work with visualisation, application programming interfaces (APIs), machine learning, and deep learning algorithms, and apply these skills to real life scenarios.
With data now a critical corporate asset, organisations that can harness data analytics capabilities to create significant value and differentiate themselves will be at a commercial advantage. With more employers than ever looking to hire data scientists and other data professionals—including business translators, data engineers who gather or manipulate data to make it ready for analysis, and statisticians—competing in a data driven world will depend on attracting and keeping a broad range of experts.
In the face of a crippling talent shortage, accelerating learning to give professionals the relevant data science skills and mindsets they’ll need to harness new data sets and analytical techniques will be key to ensuring the enterprise has data professionals with the know-how to perform—and are incentivised to stay and grow their skills.