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The Inside Scoop On Business Analytics Programmes

Business Analytics

Discussions on implementing big data in business tend to start with the technology. People tend to think about the software that’s needed, where the sources are to be stored, the tools used to collect data, the platform that will power the analytical ‘crunching’ and more. Technology is certainly a crucial for any business’ analytics strategy, but it is equally vital not to neglect or misjudge the organisational and human elements needed to make the practical application of big data a success.

The business intelligence and analytics software market is booming. Business leaders the world over are certainly fuelling market growth with investment in technology and personnel. Industry revenue is expected to reach $18.3 billion this year, up 7.3 per cent from last year, and continue increasing to $22.8 billion in 2020. The stakes are high for data to supercharge business growth.

However, a successful business analytics programme comprises more than just a bundle of powerful technologies or a group of data analysts sitting in an office, toying with newly bought software. To unleash the power of these solutions, they must be shared out across departments in the organisation: From HR and finance, to marketing and sales. Data needs to become the beating heart of the whole organisation, informing stakeholders and the key decisions they make. If not, benefits will be siloed and may not achieve the ROI they should. It can be a challenge to overcome, and there is a lot of advice out there on how best to do it.

What tends to happen in a business where the analytic process in unplanned, is that employees begin to do their data work in siloes. Empowered by the new easy-to-use software, users are excited by their newfound ability to solve complex problems in a matter of minutes. However, without proper guidance from middle and top management, these enthusiastic, self-made data analysts can get stuck into their own siloes, rather than stopping to look to other colleagues for inspiration.

The business tends to wake up to the fact that they have pockets of analytics skills, but may not be using them to their best advantage. There might be a ‘diaspora’ of analytics skills spread across the business, or clustered in one team, depending on how much headway the new way of working has made. Now that the new easy-to-use analytics software has empowered employees to do great things with data at a personal level, their passion for data needs to be embraced by the C-suite.

But how does the business turn the siloes of enthused data lovers into a broad resource of experts embedded throughout the business?

From Many Comes One

To give one example, in January 2015, Ford formed the Global Data Insights and Analytics (GDIA) unit. This is a centralised data science team tasked to share analytical best practices and to spread optimised, data-driven decision-making across the organisation. Growing from a staff of one to more than 600 professionals, GDIA shares its expertise across every aspect of Ford’s business, including human resources, manufacturing, research and development, warranty, supply chain and accounting. GDIA is also applying its expertise in emerging areas as Ford expands to be both an auto and mobility company.

There are simple steps that any size business can take to harness these levels of data devotion. To better properly democratise data assets among all employees, it’s a good idea to set up a business analytics resource of some description. This can serve across all the business departments and guide line of business analysts with best practice and better governed data – but allow analysts the freedom to use the data the way they need to.

A ‘chief data officer’ or any central analysts will need be flexible, looking after the data needs of the entire business – breaking down silos between specific departments. Line of business analysts should be free to use and query the data they need to, wherever it comes from. That means the whole business needs access to, and understanding of, the data resources within the business.

In time, line of business analysts may also access specialists when they need. These experts can assist them with the heavy lifting such as advanced statistical modelling, which the business may need as its data journey matures. In this position, the analytics resource can share its invaluable talent across the organisation. In turn, as line of business analysts gain broad knowledge of all the available data across the organisation, they can bring new ideas to each department, informed by the business’ overarching goals.

In turn, IT departments need to reassess their function in the business and focus on what their best at. Now that the business is heavily focussed on the centrality of data, the IT department needs to develop resources to allow for this to happen. This entails providing ‘data workers’ with the resources they need, whether that be compute engines, software, regular relational databases tuned to work at their absolute best, or big data platforms for longitudinal or predictive data analysis. IT needs to give up its former ownership of data and the applications that surface it (Data-as-a-Service) and focus on providing platforms and resources (Platform-as-a-Service). That way, they can ensure that everyone can access and work with the company’s data at any time.

Modern business intelligence and analytics solutions aren’t as rigidly centralised as in times past (in fact, some are thoroughly democratic). However, a reason to re-centralise (to the degree of having point of authority for coordination and governance) is driven by the fact that there is data that needs to be used that is inherently cross-department – or at least needs to be tapped into by departments other than those that collect it.  HR need to tap into sales data in order to analyse sales team performance, for example.

For the departments on the receiving end, the centrality of the data can also mean less work and more transparency. For example, the marketing department need not study the popularity of a particular product in the last year because the sales department already has that data to hand – and both teams are talking and sharing data across the business. This kind of culture avoids redundant work and reduces costs. What’s more, as stakeholders draw from the same pool of information and apply consistent data governance practices, they evade the corruption of data and do away with multiple versions of the truth.

By allowing all levels of employees from all departments to come into regular contact with data analysts, they no longer approach analytics with the mind-set of generating an individual result. Rather, with a better understanding of data’s capabilities and nuances, employees come to appreciate the true power of data. Then, instead of generating dashboards and reports on one-off occasions, they use analytics to solve broader business challenges. As a result, every penny spent in software, data sources and personnel is directed at the overall goals of the business.

When it comes to initiating a business analytics programme, the key word to keep in mind is ‘centralise’. Centralised co-ordination and oversight promotes consistency and sharing of best practices, as well as internal transparency and collaboration. In this way, not only do businesses benefit from an efficient way of working with data. With an open channel of ideas flowing throughout, accommodating each department and each team which sits within, the business is also in a position to spawn new ideas and fresh strategies which remain in line with the company’s core aims.

Nonetheless, when it comes to instilling a stronger culture of analytics, it ultimately comes down to the CEO. If the CEO ignores the recent developments in analytics and omits data discussions from executive meeting agendas, then their business will not be able to take advantage of today’s computing.

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Chiara Pensato leads marketing in EMEA for Alteryx, the leader in self-service data analytics. Additional insight provided by Craig Bloodworth, CTO & Tableau Zen Master at The Information Lab.