At the foundation of this transformation is the need for data and the ability to derive insights from it. Investing strategically in data with analytics functions delivers rich dividends to the enterprise. Some companies with an internal focus have achieved operational efficiencies and reduced costs. Others with an external focus can find new markets, engage closer with customers, and improve their growth trajectory. Additionally, some enterprises have utilized their existing data assets to develop advanced capabilities that have become an additional business revenue stream.
There is a data leadership evolution happening today in which organizations are struggling to build, upgrade and manage their data function. In this article, we will discuss ways in which organizations can structure the function, attract stellar talent, and retain them. Strategize your data capabilities and requirements across the enterprise
Leveraging data can result in significant advantages at almost every level of the enterprise and the organization strategy. However, attempting to build capabilities across all the opportunities, simultaneously, can detract from the strategic priorities. It is crucial to identify the areas from which the most impact can be gained and more importantly build a strong leadership structure, to support and achieve those goals.
Comprehending where an organization is in its data journey is essential to identifying the leadership talent that is required. Technology oriented talent who are ready to build data platforms and single data lakes are ideal for companies in the early journey. Entities who are mid-way could be looking to expand their analytics capabilities or experiment with data science, and those in a more mature stage might want to bring in commercial P&L or experimental AI leaders to enable them to go to market with their analytics and data capabilities.
Create the optimal organizational structure
The data function can be structed using multiple models and there is no one size fits all solution. However, there is a definite trend towards the ‘hub and spoke’ hybrid model being witnessed today. This allows for a balance of market and enterprise remits, a more enhanced position within the enterprise structure and stays close to commercial opportunities. The Hub and Spoke Hybrid Model
In the “Hub and Spoke” Hybrid Model, data and analytics talent reports into a single leader, and function as one team while each business can pursue market adjusted D&A initiatives. Through this method the data leader has authority and can augment data governance and quality while focusing on key organization priorities. This model facilitates transformation, while allowing for innovation and best practice management. In addition, the proximity to market and commercial opportunity is not hampered.
Some of the commonly encountered questions that are posed when discussing the development of a data function are:
Should data report into technology?
This typically is a connected to the level of maturity of the enterprise. Businesses with different data systems wanting to build their foundations from the ground up will merge their data and technology functions. Advanced organizationsare more likely to separate data from technology. In tech-first and digital platform organizations, data is very likely to be embedded across the organization organically, and may not have a central combined figurehead, but instead a chief data architect or technology lead to oversee the underlying structure.
What impact does structure have on talent?
The ability to attract talent is determined by the structure of the data team. Today, CIO’s bypass positions where they are not the owners of data. Contrary to that, data and analytics leaders are more comfortable being closer to business units and commercial opportunities, rather than being enclosed within the tech function.
Should analytics and data sit together?
Traditionally data and analytics were grouped together within technology, but recently we see a trend of analytics splitting from data and moving closer to the business, to provide insights. Data management on the other hand is still under technology leaders. What we witness now is a hybrid model of this, where there is an understanding that data has to be close to the business, but that there is also valuein having advanced data and AI capabilities sitting within the technology function.
How to identify leadership for the data function
Robust leadership is critical for success in the data function, and it involves a mass of talent. Typically, the data leader would have extensive experience across the whole data lifecycle, even if the role was split into a chief analytics officer with an external insights and opportunities focus or a chief data officer with a focus on internal management and build responsibility.
When looking for talent, remember:
- Great talent does not come only from established/ advanced organizations.
- Find talent that have the ability and flexibility to match a roadmap while still retaining the capacity to innovate.
- Brilliant data leaders should be able to translate technical jargon into business speak and vice versa. They should have both technical and commercial skills.
There is a massive demand for data talent across all industries, which has in turn considerably increased the competitiveness of attracting good talent. A combination of brand and compensation undoubtedly helps. However, it is also important to have a sturdy yet passionate narrative for the company on where it is now and where it will be a few years’ time. Last but not least, it is vital that honesty is a quintessential part of the recruitment process.
The author is Managing Director, Russel Reynolds Associates.