It’s been okay in the last couple of years to be a self-proclaimed ‘data-driven’ organisation. By this, an organisation may say “we make our decision using data”. Now, do not get me wrong. It’s a great idea to support decision-making processes with data. But there is something insidious in this statement, as it does not focus on the need for “analysis of data” but the mere “availability of data”.
Hence, such organisations do not recognise the need for data scientists, data/business analysts, software engineers, artificial intelligence and machine learning powered technologies with robust management information systems.
Becoming an ‘analytics-driven’ organisation is to recognise data as the ‘New Oil’ and a gateway to new opportunities. Data has been shown to be limitless, as the amount generated keeps increasing at an exponential rate; largely because of the growing number of connected devices and the world’s increasing interest in Internet access.
According to International Data Corporation (White Paper- Data Age 2025), 2.5 quintillion bytes of data is generated per day – i.e., 1,000 petabytes. Some surprising findings in the report include: Internet of Things (IoT) data being the fastest-growing data segment, followed by social media. It is estimated that global data volume will expand 175 zettabytes by 2025, and 90 zettabytes of this data will come from iot. Meanwhile, Forbes predicts that 150 trillion gigabytes of real-time data will need analysis by 2025 for sustainable business growth.
These above statistics have surged Chief Data Officer (CDO) appointments across most industries. PwC’s study in 2021 on CDO reveals that 27 percent (from 21 percent) of the world’s leading 2,500 public companies have employed CDOs with executive level responsibilities, and banks and insurance companies are among the top three (3). This, according to PwC, correlates with the strong financial performance of these companies – reflecting the growing value for ‘analytics’ of data. If data alone were enough without analytics, there would not be any need for a surge in personnel to handle the space for growth.
The question then is: How do you ensure an effective data ‘analytics’ drive in your organisation?
Here are a few steps:
- Set a clear vision toward transformation
Whether outshining competitors or increasing profitability, data analytics transformation is a crucial part of business strategies in modern industries. If there is a looming decision such as this, it is often hard to know which direction to go. But you cannot be indecisive, as huge companies are likely to be marginalised or plummet in sales over the medium- to long-term.
There is therefore need to develop a clear and shared data vision and mission statement toward the organisational data analytics transformation effort. This is the most important step toward the transformation agenda. The vision statement provides answers to “Why the need to harness data and the analytics of the data harnessed?” The data vision will provide alignment for the short- to long-term goals and offer a baseline for your data strategy.
The data mission will define “What you are going to accomplish and How you are going to accomplish it”. The data mission will also sit as an important starting point for your data strategy. The strategy will incorporate the skill set or capabilities needed; the analytics technologies to leverage; and the relevant Management Information Systems as adjunct tools, as well as training and capacity building.
Technologically naïve companies will need an Enterprise Strategic Information Management document to aid in setting actionable goals.
- Creating a data culture at scale
Create a culture of innovation that positions data at core of your business. “Culture still eats strategy for breakfast”. This aphorism is attributed to legendary management consultant Peter Drucker, and it certainly holds true for data transformation efforts. Making a commitment to data analytics transformation is one thing, and executing on that commitment is quite another. For most data analytics-driven organisations, harnessing and understanding data is an asset and must be valued by everyone as such.
In some organisations, it has become the frontline centre that is depended upon by traditional frontline staff like marketers to make customer-centric decisions. The third-wave of analytics has shown that all employees, not just data science experts, need to be mindful about the essence of data analytics with contexts that are relevant to their business processes and problems at hand. Data Integrity is the hallmark of every data analytics-driven company. Data culture makes data integrity everyone’s business in any organisation: from the one who collates to the one who mines, and to the one who feeds on insights to make informed decisions.
- Get the right technologies and people
Technology alone is not enough to transform an organisation into a data analytics-driven dynamo. There is a need to have the human resources that are very much convinced data could possibly aid an entity to identify opportunities, solve problems and make informed decision to help create superior value for customers and shareholders.
The evidence of being a forward-looking data analytics-driven organisation is the harbouring of empowered data scientists, data engineers and data/business analysts who see data as an asset and not a myth. The glut of data presents new opportunities for organisations, but the current infrastructure coupled with limited data scientists has made it challenging for companies to extract the needed value from data to inform business decisions.
A survey by ChaosSearch 2021 revealed that IT teams spend almost as much time prepping data (6.6 hours per week) as they do analysing it (7.2 hour per week). That’s clearly productive hours wasted, as this process can be fully automated in a bid to alleviate the talent shortage.
Fortunately, with challenges come new opportunities. It’s however worth noting that managing big data from different data sources requires more than traditional IT and analytics platforms. Analytics technologies such as Jupiter Notebook (Project Jupiter), Python (Python Software Foundation), SuperML (SuperFluid Labs) and Power BI (Microsoft) are some enablers (modern) for high-powered data analytics initiatives driven by big data which require minimal IT proficiency.
Remember!
“Don’t invest big yet. You must be strategic in employing these technologies and prioritise cost optimisation. You may want to start small by identifying a few but immediate-use cases that are relevant to your daily work activities. Think security and data confidentiality as well.”
Disclaimer: “I am not an agent or in any way linked to the above technology owners, and neither do I intend to pitch these technologies to anyone who would want to subscribe”.
In summary, global migration to digital channels presents a lot of digital footprints which bring enormous opportunities to be harnessed and mined for sustainable growth.
The concept of a ‘data-driven’ organisation is no silver-bullet here. Becoming an ‘analytics driven’ organisation will hinge on how organisations view analytics. A lot of firms have begun to think about what it would mean to be data analytics-driven, but few have developed and articulated a corporate commitment. There are still more opportunities for early-birds to reshape their business landscape to enable the emergence of new business innovation for competitive edge and supercharging performance.
In essence, analytics enable an organisation to effectively grow, optimise and protect value.
>>> Michael B. Nyantakyi is a professional banker with over 5 years of experience in banking, and a part-time lecturer of Bank Strategic Management Information Systems and Law and Practice of Banking at Sina Consult (Licenced Chartered Institute of Banker Tuition Provider). He also has some experience in data analytics through his practice as a Business Analyst and Business Development Lead with a Ghanaian and Kenya-based Data Analytics and Technology Firm. Contact: 0202970980. Email: [email protected]
