1. Transcending traditional organisational barriers
Data is no longer confined to technical departments. Successful projects must be partnerships between lots of different groups with different goals, mindsets, levels of understanding and ways of working.
Data leaders must have a strong understanding of the business, their industry, their data and what their data represents. They must be able to direct teams which encompass data scientists and analysts who can understand the data, business people who can frame the problems that need solving, IT people who can put it all to work, and domain experts to ensure that the insight is clearly presented, while making sure that everything links back to the business’s objectives.
‘The best data leaders will be able to act as translators to bridge the gap between these different parties – speaking the language of the business and the data scientists,’ says Matt Jones, lead analytics strategist at Tessella.
2. Merging science with business
A true data leader is not an IT director in the traditional sense; nor do they need to be an expert in advanced data analytics. The infrastructure and technology are necessary elements, but the core challenge in becoming a data-driven enterprise lies outside this scope.
Instead, a data leader should be a manager who is capable of doing one crucial thing: merging science with business. Integrating new types of data analytics into business requires significant changes in the usual approaches. Existing processes and organisational structures are often not tailored to work with automated and truly data-driven decisions. The role of a data leader is to enable this.
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When these are algorithms making the predictions and recommendations, the only way to demonstrate and measure their effect is through experiment, carried out in accordance with all formal requirements.
‘This approach may be well known to a scientific facility, but is quite unusual for a commercial organisation,’ says Jane Zavalishina, CEO of Yandex Data Factory. ‘Establishing the environment and processes that would allow for such experimentation is crucial to actually derive business value from data-related initiatives. Only with a rigorous, scientific-based approach and by having clearly articulated success metrics in data projects can the organisation learn to trust the black box predictions instead of PowerPoint presentations and gut feeling.
‘This requires overcoming existing organisational boundaries, such as deciding how responsibilities are assigned and data is shared between departments. This urges for the ability to lead the dialogue with business owners to convey the meaning of the technology and identify the best data use cases that could actually be carried out in the existing company environment and bring directly measurable effect.’
A true data leader should have a clear understanding of business needs, have a solid knowledge of tools and technologies, and be an excellent manager and communicator to align the two.
Big data is an area of work in which there are so many unknowns about real-world commercial outcomes, so much to ingest and discover, that you need a certain level of ‘guts’ to be involved full-stop.
For many big data companies, given the sector’s youth, there is still a lack of a track record, and this is one of the biggest challenges. The business world craves certainty above all, and with certain incipient big data technologies there can still be a step into the unknown.
‘Taking this step as a data leader shows courage, in my opinion,’ says Luca Primerano, director of strategy at Fortytwo Data. ‘After all, while data leaders have to answer for every pound spent, it’s not necessarily clear what opportunities will be driven in the future by being able to analyse, in a more joined-up way, the hidden intelligence within the billions of data points collected each day by the largest firms – often from countless siloed divisions.’
Data leaders have the responsibility to spot these trends and work to ensure that data is used to its fullest potential.
4. Creating a data culture
It’s important to give data teams the room to think creatively when they are interpreting vast quantities of data. Models and visuals can be useful on their own, but human analysis provides the real value and contextual insight, often sparking new product ideas, services or business approaches.
When building a data team, it’s important to look beyond quantitative skills alone to include experts with a range of perspectives, including computer science and business analysis. That said, creativity will be wasted if resources are not put in place to support new projects.
‘Leaders should aim to create a culture in which teams can explore meaningful threads, and then focus on driving evidence-based outcomes,’ says TJ Hannigan, head of data insights at Dropbox. ‘In a world in which industries are constantly being challenged, data leadership is crucial to agility, strategy and, ultimately, competitive advantage.’
5. Separating hype from potential
In the era of big data, businesses have been told to save every scrap of data because it might turn out to be a valuable clue to customer understanding or business performance.
While the cost of storing data has fallen dramatically, it doesn’t represent the true cost of data, which includes ensuring quality, maintaining freshness and mitigating the risk of a breach.
Data leaders are astute at weighing the potential of a data source or type and determining whether it is worth keeping, even if the present value of it is unclear. They know that they need to justify, explain and place value on each piece of data.
‘Data leaders are equally discerning about the hype and potential of the analytics used on their data,’ says Scott Zoldi, chief analytics officer at FICO. ‘They focus on the problems the business needs to solve, and investigate new analytic techniques that can make their data “talk” in increasingly valuable ways.
‘They will be exploring artificial intelligence and machine learning, but won’t buy into the hype that machine learning plus big data will magically solve all their problems. Anyone can find patterns in data – data leaders do the work to ensure that they’re finding meaningful, actionable patterns, not arbitrary correlations.’
6. Asking the right questions
Data leaders must ensure that they, and those who use data in the business, have a clear and focused question they’d like to answer.
Too often, organisations don’t know where to start with the vast amount of data they have at their disposal, and try to solve everything at once. With this approach, it very quickly becomes a daunting undertaking for a company, and this usually leads to no questions being solved at all, and a sense of data apathy.
By comparison, a data leader starts by identifying the right question to ask, surveys what data is available, and is then far better equipped to allocate the right resources to solve it.
‘The key is to start small,’ says Dr Kim Nilsson, founder and CEO of Pivigo. ‘You don’t have to solve everything at once. Being a data-driven business doesn’t mean you have to dedicate months and millions to get value. A small group of data scientists in five weeks can really get the low-hanging fruit
that would immediately make a big difference to a business.
‘Fundamentally, the hallmark of a successful data project is to ensure that you are asking a question that will lead to a business outcome. Data leaders realise that data science is not just an academic exercise – they are results driven and ask the questions that will lead to commercial value.
7. Vision and purpose
Most businesses today are paralysed by the amount of data that they have access to and are not doing the most to use it to their advantage. A data leader needs to take responsibility for defining a use case for data that will deliver real benefits for both the customer and the business.
This approach justifies board-level backing for investment in outcomes, overcoming this ‘data paralysis’, and often leads to unexpected new insight for the business to act upon.
A true data leader is responsible for constantly evolving a business’s shape, size and offering, in response to hard data showing where any blind spots or opportunities may lie.
‘Data isn’t a vanity exercise – it’s about spotting trends and opportunities, and driving growth, efficiency and change in a business,’ says Bill James, chairman of digital transformation consultancy Transform. ‘Having a clear purpose and use case for data initiatives aligns business effort towards an end goal and helps make sense of the vast amounts of data available. This ultimately means that businesses can deliver a better customer experience.’
8. Understanding the revolutionary potential of data
All data leaders understand that it’s now or never for their organisations to be part of the digital revolution, or else be left behind. This means rejecting incremental improvements and small thinking and instead focusing on changing the way that people go about their day-to-day lives.
Whether it’s an application that connects medical professionals with their patients or Amazon Go’s checkoutless vision of the supermarket, the end-user should always be at the heart of the digital revolution. Likewise, data should be fundamental to improving the customer experience.
The revolutionary potential of data is often spoken about, but most organisations can only use it to deliver minor or superficial improvements to existing services. Where data leaders differ is that they recognise that the ultimate end goal is not the service but the people using it – using data to engage and provide an experience that would be impossible to beat or replicate.
‘The organisations that are thriving today have long known that their customers want to interact with them through mobile, web and IoT applications first and foremost, and that their experience doing so will be the difference between success and failure,’ says Couchbase principal architect Perry Krug. ‘They understand that it’s about the journey as much as the destination and focus on entire digital experience, not just the final business transaction.’
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9. Enabling data to move faster
Data is the fuel of modern business and holds a tremendous amount of untapped value, but it’s also big, heavy and slow. It is not unusual to wait weeks for fresh copies of business data, running up to months in the worst cases. In the context of continuous innovation or big data analytics, this destroys any chance
for agility and speed.
If this wasn’t bad enough, securing or anonymising personal data adds even more time to data delivery. To work around this, companies use data that is often months old, directly impacting quality of testing and limiting insight from trend analysis. The secret to being a data leader and staying ahead of the competition is enabling data to move at the speed of business.
‘This relies on developing an organisation’s ability to ensure consistent availability, quality and security of data and allowing easy movement around the business,’ says Jes Breslaw, EMEA director of strategy at Delphix. ‘Modern data platforms are able to deliver unlimited complete copies of secure business data in minutes, not weeks or months.
‘With data no longer a constraint, projects are not only accelerated but run in parallel as data is no longer a bottleneck. Changing how data is delivered presents an incredible opportunity for business.’
The world is in a constant state of flux, and most industries are faced with the same challenge: the task of managing and making use of an ever-growing flood of company data. Large quantities of this data are being siloed, discarded or even lost in the IT system, where its potential is lost.
Organisations can benefit hugely from big data, but only if their data leaders implement committed forward-planning practices within clear data strategies.
‘The leaders that are benefiting from analytics look to the long term and recognise the importance of continuously improving their data management strategies,’ says Laurie Miles, director of analytics at SAS UK and Ireland. ‘This leads to cleaner, better organised and more useable data, allowing leaders to overcome the volume and gain maximum insight from their data while innovating existing business processes through superior analytics.’