Taming your data to cross the analytic divide & other CDO priorities amid a business reset
"To be successful, the CDO must ensure that..."
The pandemic kick-started one of the greatest business resets in modern history. From better understanding customer sentiment, to reshaping operational efficiencies and planning for the unknown, the last year has forced businesses to accelerate digital transformation projects and become more data-centric to hone their competitive edge, writes Alan Jacobson, Chief Data & Analytic Officer, Alteryx
Business’ response to the pandemic also shone a spotlight on the growing analytic divide – the difference between those businesses that can quickly leverage data-driven insights and automate processes in response to major global disruption, and those that cannot. Data remains one of the most important assets of any company. For many businesses still trying to extract value from their data, the benefits of analytics remain out of reach.
For companies wishing to cross the analytic divide and mitigate further disruption, a number of organisational shifts must occur to ensure the protection and proper utilisation of their data.
As global advisory firm Gartner notes, the success of a digital-first business now depends on all employees being information workers who can “speak data”, making the ability to identify and communicate key insights vital for company success. With an endless supply of data available to businesses, as an agent of change, the role of the Chief Data Officer (CDO) has become vitally important for organisations looking to stay competitive and develop a much-needed data-driven culture. CDOs ensure that data patterns presented to decision makers are not only delivered in a way that can be understood, but also deliver data-led insights by providing them with the right data tools to make that goal a reality.
Driving digital transformation
Many forward-thinking companies have already appointed a CDO to oversee their digital transformation. The CDO will make decisions regarding investments in data teams and analytic capabilities. Their objective is to harness the workforce and ensure everyone is on the same journey. By providing analytics solutions that upskill information workers into data-literate knowledge workers, these knowledge workers - individually and collectively - can drive organisational transformation.
Given the right technology, every employee can innovate and drive huge breakthroughs; opening up the power of data science and analytics automation so that workers can deliver business changing insights. With this in mind, the first priority for a CDO crossing the analytic divide is to democratise data across the organisation.
In the context of business, it’s about scale… providing data access to three analysts in a 300-large employee base will not make a company digital-first. It will help, of course, but their influence will inevitably be limited. The CDO needs to bring everyone in the company along on the journey by democratizing the data needed, providing access to one centralised platform, and amplifying human intelligence so analytics can be transformed into an asset that can help inform teams and departments in any part of the business. This will help teams to make better decisions in real time and — most importantly — respond with agility to disruptive events.
See also: The Big Interview with Mastercard CDO JoAnn Stonier
The next priority is to educate and train the existing workforce and develop their data literacy. Opening up the world of analytics to the entire workforce by embracing a human-centric approach to learning and providing space for employees to explore and learn about data analytics. To succeed, digital literacy is about equipping people who are not qualified data scientists or experienced analysts with intelligent tools that will allow them to work in the same way to deliver the same outputs. Once siloed these data science teams can then act as guides and, when both groups speak the same language of data analytics, they can pull out trends and discover insights to make faster, better, more informed decisions.
This always-on approach to upskilling helps to address the data skills gap in the labour market. Britain is facing “explosive demand” for data science skills and, while not every worker needs to become a data scientist, most will need a basic level of data literacy to operate and thrive in increasingly ‘data-rich’ environments. A recent UK government report found that almost half of businesses (46%) have struggled to recruit for roles requiring hard data and IT skills, which are essential to any successful digital transformation project. Shaping tomorrow’s world by developing the talent of today can contribute significantly to existing employees’ skillsets, solving skills shortage issues in days, not years.
As such, the third priority for the CDO must be to encourage this culture of analytics within the business. Are workers asking the right questions of the data to enable transformation? Do employees feel empowered to use analytics to change current processes in a way that will generate value? Those closest to a process know where the problems exist, and by amplifying human intelligence to get the best from data science and analytics, they have the context and can see the business impact of solving that question through data.
A final priority for the CDO is to invest in the organisation’s data ecosystem. Democratising data and upskilling workers so they can analyse it will be insufficient if said data is of poor quality. Today, the amount and increasing complexity of data available from various sources is increasing every day, and the research firm IDC estimates that 90% of all data created is “dark data” — information that is unstructured and inaccessible. Therefore, CDOs must spend time and money cleaning the data and engineering processes to collect the right data to generate actionable business insights.
How to know when it is time to act
With these priorities addressed, it is time to begin crossing the analytic divide by finding a problem and empowering the team to solve it. Which processes are less effective than they could be and how might a data analytics-led transformation benefit the business? Let’s consider the shipping of goods, for instance. This is an area of business that was significantly disrupted not just by Covid but also by the Suez Canal blockage earlier this year.
A major manufacturer will likely have thousands of products to ship, and each product has hundreds of parts that must be managed to ensure no disruption to production. The standard shipping times for all parts will have been entered manually into the fulfilment system and, based both on the demand forecast and the speed of production, orders are made to ensure parts arrive before running out. Everything works smoothly until – suddenly - the supply chain is disrupted, and shipping times begin to change.
Once this process proves insufficient, how then does the manufacturer adapt and recover? This could be done manually by a team of people, but it will take a huge amount of time and the work will need to be repeated if shipping times change again. Alternatively, a simple analytic model could be built that monitors the shipping times in real-time; automatically updating the system with correct data.
This is one example, but the same thinking can be applied to any function within the business. These individual data transformations will ladder up the overall digital transformation journey and eventually help the company scale the analytic divide.
The CDO is key to this transformation. To be successful, the CDO must ensure that data flows through the company and that all data workers have equal access to the skillsets and resources needed to analyse it. If integrated correctly, the business will have moved to the right side of the analytic divide and will be prepared for any unexpected disruption due to the flexibility and resiliency of the teams and strategy put in place.
