on May 07, 2018
90% of the world’s data was created in the past 24 months. Regardless of whether your business operates in retail, manufacturing or finance, the explosion of data creates unprecedented opportunities, and challenges, for companies looking to stay ahead.
The benefits of using data creatively and effectively are clear: improved business performance, innovative data products, informed decision making, metric transparency, operational optimisation and even customer analytics. Companies that successfully marry culture and data insights drive revenue growth, customer satisfaction and employee productivity.
Nucleus research found that companies can generate over a 1,300% Return on Investment (ROI) in business intelligence and data-driven implementations. One only has to see the results of integrated data science and engineering teams fuelling success at LinkedIn, Facebook, Airbnb and Amazon to admire the competitive superiority and agility made possible with the right data talent and incentives in place.
Dr. Patil, LinkedIn’s former Chief Scientist (who coined “data science”) defines being data-driven as an “organisation that acquires, processes, and leverages data in a timely fashion to create efficiencies, iterates on and develops new products, and navigates the competitive landscape.”
If being “data-driven” is so advantageous and cutting-edge, why do so few companies actually adopt such a culture? McKinsey recently surveyed 3,000 A.I.-aware executives across 10 countries and 14 sectors to find that only 20% of respondents “currently use any AI related technology at scale or in a core part of their businesses.” It seems awareness is far from the bottleneck with media outlets hyping daily the buzz-words of big data, data science, machine learning, deep learning, automation etc..
As with any transformation, driving behavioural shifts require effort, persistence, failure, time and investment. Being data-driven is not a switch and reaping the rewards of integrating data-analytical talent and business processes is hard work. Leaders have to consistently press teams with articulated business challenges grounded in data mining, analysis and extrapolations. Executives need to continuously harp the importance and urgency of unleashing the richness of the data.
The challenge is inertia. While its easy to highlight the importance of digital transformation in meetings, its even easier delay the priority of a initiatives quarter-to-quarter. There are myriad priorities blockading the important from the “urgent”. We are inundated with meetings and day-to-day operations creating difficulties in truly materialising the big questions involved in investing in longer-term artificial intelligence initiatives that can build a scalable competitive advantage.
Yet, its the responsibility of leaders to combat the inertia and become true champions for technological progress and a data-centric cultures
Going from data-resistance or data-curious to data-guided or data-savvy is where most companies bottleneck due to a lack of legitimate buy-in. This push is a continuous process to build an initial momentum that can overcome the bottlenecks. We have to commit to the initiatives and eradicate the inertia by landing quick wins.
Typically, once leaders are ready to inspire urgency for business acceleration and evolve away from the resistance of legacy practices they will need to address the talent equation. Knowledge cultivation and talent supply is more essential to adoption rather than worrying about toolkit and model choice. Assembling such talents is time-consuming, complex and costly on an individual basis let alone to form a team. It does not help that data-scientists are highly in-demand with job-postings at an all-time high. Not to mention costs associated with investment in data collection, cleaning, hosting and maintenance that is required at these stages.
Successful data initiatives are made only possible with an analytical team that possesses multiple skill-sets, roles and perspectives. Teams are built of data analysts, data engineers, data scientists, machine learning engineers and revolve around combining domain knowledge with digital transformation.
If the surging demand for data scientists and uncertain return on investment causes you to blanket at the possibility of corporate reinvention, how do you rethink the talent war affordably? Companies scrambling in this zero-sum fight for hiring data talent are better off investing in data-science training to upgrading in-house talent to build a critical mass prior to on-boarding costly subsequent hires. Not only does such investment increase employee productivity and retention for top-performers, the right tools and technologies empower your current talent pool to become passionate evangelists for promoting a data-driven culture from within. Aside from reduced turnover and recruitment fees, training talent that already understands your business drives a confidence and sufficiency by focusing on experimentation, innovation and efficient workflows.
By using practical data training initiatives, companies focus on quick early wins coming from data analytics that can slowly compound at a fraction of the price and brain damage of external talent acquisition. The answer is simple. Find the existing programming, analytical and quantitative talent in your organisation and infuse this pool with the latest data-science skills and tools such as Python, PowerBI, TensorFlow, Tableau, Hadoop, Spark and SQL.
Even better, spread data education cross business, product, marketing, engineering and supporting divisions to bringing together talents with various backgrounds through data literacy and enable tighter integration opportunities. Even the business staff are often analytical and hungry to learn, adopt and drive analytical workflows in their day-to-day.
Airbnb’s internal Data University is democratising “data education for anyone at Airbnb that scales by role and team…[with a] vision to empower every employee to make data informed decisions“. After training over 500 unique employees (almost 1/8th of the workforce), Airbnb found that “creating “citizen data scientists” is powerful — not only does it help ensure that decisions are grounded in data, but it enables people to make decisions autonomously”.
When applied correctly, data science training is a low-hanging fruit that prepares organisations to begin scaling data-culture. External hires such as data scientists, engineers and analysts can always be brought in to evolve initiatives into machine learning and deep learning after scoring quick wins and momentum.
Solving the skills challenge can be done in a variety of ways. However, in order for data education to materially benefit business performance, the up-skilling has to address the skills gap in the context of a business or analytical challenge. Many companies try to throw $9 machine learning course video-based learning subscriptions on online Massive Open Online Course (MOOC) platforms (e.g. Udemy, Coursera, Udacity) at their employees and watch in amazement when engagement levels are “surprisingly” low. The real value is in educating your teams with an outcomes-orientated approach in the context of your data sets and the projects that are most relevant to the stage of your data-driven journey. This stage is unique to every company and culture.
We recently engaged with a multi-national corporation with several thousand employees and multiple business subsidiaries to start upskilling their most talented employees and build a multi-channel talent pipeline strategy. By designing a series of tailored modules, data-sets, projects and studies, Xccelerate is working with the leading enterprise to critically address the areas of opportunity where it can harness its own talent with custom learning pathways and build a multi-faceted analytics team and cultivate product champions. The talent development engagements have been spectacular with the employees finding they are more effective and productive in their roles. We look forward to evolving our work with this enterprise and others across industries in building talent eco-systems.
There has never been a better time to invest in your talent and being building your in-house data-capabilities. From product experimentation to data visualisations and model-engineering, robust data science and machine learning initiatives always begin with small teams and quick wins before incrementally reshaping disproportionately.
Founded on the vision that the greatest opportunity for improving lives is education, Xccelerate aspires to reinvent tech education and hiring to bridge global talent gaps in Artificial Intelligence, Software Engineering, BlockChain and Design. We achieve outcomes for individuals and enterprises by leveraging our expert instructors, proprietary curriculum, customisable corporate training programs and deployable learning platform.
Till date, Xccelerate has trained 700+ professionals and boasts a 91% hiring success rate in our immersive programs. We have powered enterprise training and graduate placements to various industry leaders including AXA, Zeroth.ai, Standard Chartered, Kaplan and ANX. We help companies Xccelerate their go-to-market speed for data-initiatives with focused data-education and data talent on-boarding to navigate through the early inertia.
If you are interested in upgrading your team’s technical capabilities, feel free to reach out or learn more about us at www.xccelerate.co.