Although data analytics technology has been around in one shape or form for years, it seems that organisations are still having problems in obtaining the full value from their often rather pricey initiatives.
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The The age of analytics: competing in a data-driven world report, released by the McKinsey Global Institute in December 2016, revealed that while a few – mainly digital native – organisations were using their data and analytics technology effectively, most were a long way from doing so.
The most successful initiatives were found among retailers and firms offering location-based services. But McKinsey indicated that companies in manufacturing, the public sector and healthcare were obtaining less than 30% of their projects’ potential value.
Beyond greenfield sites that have been built with data in mind from the ground up, Jason Foster, founder and director of data and analytics consultancy Cynozure, believes there are three main types of organisations that are struggling to getting it right.
These include companies in which senior leaders have said they are keen to benefit from data analytics but where the business is struggling to deliver.
There are also firms that have been investing and working in this area for a long time but, because they are still not gaining value, are becoming disheartened. Finally, there are the organisations that are a mixed bag in terms of maturity.
So although their e-commerce and digital marketing departments may gain a lot of value from their data and analytics activities, more “traditional” areas such as logistics do not.
The biggest obstacles to success are not so much technical as organisational, according to a study entitled Plotting the data journey in the boardroom: the state of data analytics 2017, conducted by software supplier MHR Analytics.
It found that while just more than three-quarters of the 300 UK-based C-level executives questioned planned to undertake a data analytics or big data project over the next 12 months, the most significant barrier to doing so was finding and training appropriate staff (42%).
Another key issue was developing a coherent business intelligence (BI) strategy for the entire company (29%), followed by how best to manage BI initiatives that are being undertaken by individual department heads and using BI to generate actionable business insights (28% respectively).
Overcoming business challenges
While Cynozure’s Foster agrees that finding people with good quality skills and experience is not easy and generally requires investment in training, he also points out that “at the component level, the skills are out there, but the issue is that people try to find a few individuals to solve all their problems”.
“Data analytics is a team sport and you’re unlikely to find all the necessary skills in one person,” says Foster. “You need to have a good mix of people with overlapping skills that complement each other.”
Such a team will likely be composed both of internal staff who have been given relevant training and people who have been hired in from outside, perhaps through graduate programmes. Key roles, on the other hand, will include software engineers who can build data pipeline frameworks to correlate data from a range of sources.
Also vital are data engineers able to model data in such a way as to make it accessible to business users, and data analysts and scientists. They analyse the data, uncover insights, present them to business users in a pertinent way and then collaborate to turn those insights into action.
Last but not least is the chief data or digital officer (CDO), whose job it is to understand the value of the organisation’s data and what opportunities it offers, while also orchestrating how it is handled and ensuring that governance is sound.
As for challenges around developing a coherent enterprise-wide BI strategy that does not end up fragmenting into individual departmental initiatives, Matt Jones, lead analytics strategist at data analytics consultancy Tessella, advises ensuring that it is jointly owned by IT in the shape of the CDO and the business.
“It’s important to have an overall, joined-up strategy and to understand where you want analytics to take you,” he says.
“If you don’t, the danger is that you end up with a huge technical programme that takes years to complete, when really it’s about having a vision and delivering wins quickly and iteratively.”
The issue is that a lot of organisations simply jump in and buy technology without thinking about the business problems they want to solve or the new opportunities they would like to exploit.
“Identify your problem, work out what data you need and then think about the skills and technology you require,” says Jones. “All too often, people gather lots of data in an analytics platform and then look for a problem to solve, but that’ll only threaten your return on investment.”
Cynozure’s Foster agrees. “While building a central data warehouse should be a goal, you need to pick a suitable use case and start with that,” he says. “You don’t need every department and every data set from the outset – start small and grow.”
Aligning around the customer
Another challenge people often struggle with, Foster points out, is developing the right “cultural mindset”. This is important, he believes, as “this makes things stick – it’s about how you turn your vision into an executable plan that will resonate and get buy-in.”
Enabling this kind of “cultural alignment” tends to be particularly difficult in organisations that operate in silos, each with their own profit and loss accounts and IT systems. The secret, says Foster, is to focus business owners on something they all care about, which is generally the customer.
“Customers tend to be the thing that can pull people together around a data analytics strategy – it’s a good way to win hearts and minds and get them to start thinking in a horizontal rather than vertical way,” he says.
Another good approach for introducing change is to ensure there is sponsorship at the top and buy-in at the bottom. “Demonstrate how using data can move the needle,” Foster says.
“This may come from some very small, quick work to show ‘if you do a and b, you get c’. Then you do some PR and marketing and demonstrate value.”
Having data analytics champions in different departments to spread the word is useful in this context, as is holding events such as “show and tells”, data dives or hackathons.
“You’ve got to attack these things on multiple fronts. There’s no silver bullet and no just ‘put in that new technology and bring in a new data scientist and everything will be fine’. Where you are now as an organisation will change how you execute, so it’s not easy,” says Foster.
Case study: JLL
“What makes a data analytics project successful is having defined outcomes or using the data to answer specific business questions rather than just pulling data into a system and getting lost in it,” says Eddie Wagoner, chief information officer at commercial property and investment management services provider JLL.
The firm, which is based in Chicago but has offices worldwide, including the UK, not only makes wide use of data analytics technology internally but also employs it on behalf of its customers.
“The same thing happens in a data lake as it does in a water lake – you can get in and have fun, or you can drown and die, so you’ve got to know what you’re doing. The right people need to be involved and you have to have a specific destination in mind,” says Wagoner.
This destination could be anything from becoming more productive and saving money to creating a revenue stream.
To optimise its activities here, JLL has set up a global BI and data governance team, which is based both in the US and around the world and is looked after by its own dedicated leader. Account managers also regularly join the team when working on specific challenges faced by their customers, which include Fortune 500 companies.
“It’s a co-ordinated effort to ensure governance is in place and to avoid duplication. But also the best ideas often come from the business as they know their customers so well,” says Wagoner.
Nonetheless, he acknowledges that finding, training and retaining skilled and experienced data analytics staff in the property sector is a key issue.
“Demand for people with expertise in data analysis and BI is far outstripping supply,” he says. “The real estate industry, in particular, has not historically needed people such as data scientists and so we’ve been going to other industries to attract them to ours.”
The organisation started on its data analytics journey in 2012 when its CEO Colin Dyer publicly announced that he wanted the company to become an industry leader in BI. “It was his idea,” Wagoner says. “He saw the opportunity and laid it out as a strategic goal for the company.”
Getting the CEO on board, he believes, is key. “That’s when it’ll be most successful as it becomes a strategic priority. If not, you’re not going to get the smartest people interested and focusing on it,” Wagoner says.
Case study: Student.com
Ensuring that data teams work with the business and are actively involved in supporting its day-to-day activities is crucial if they are to deliver real value, believes Ian Broadhead, head of data and analytics at Student.com.
Student.com, which was set up in 2011, is an online marketplace that helps international under- and post-graduates find residential accommodation in locations around the world. The company employs data and data analytics in most areas of its business to spot trends and monitor performance at all levels.
As a result, it holds enterprise-wide “huddles” to share company updates with its 100 employees in London and Shanghai every Friday.
But it also holds voluntary “data huddles” of up to half an hour each Tuesday, in which the data team presents its weekly findings on what is doing well or not so well and what has changed. Feedback is encouraged and senior leaders are available to answer any questions.
“It’s a great way for us to share information in a concise way and to mention if we see any issues that need flagging,” says Broadhead. “For example, there was a sudden drop here or a massive increase there, and so maybe we need to look at this or do that.”
But he acknowledges that presenting data in a way that is simple to understand for everyone – from well-established technical staff to a receptionist that has just recently joined the company – is challenging.
“Techies often just produce charts and try to explain them in detail, but they can end up losing their audience. What you need is for your team to provide context and translate the data in a way that the business can understand, using business language. So it’s about having good presentation skills too,” Broadhead says.
To ensure his data team – which comprises of two data analysts, one data engineer and himself – is suitably integrated into the business, each has responsibility for different business areas and locations worldwide and takes part in regular meetings with other stakeholders.
As to why all too many enterprises fail to gain value from their data initiatives, Broadhead believes there are a number of reasons.
On the one hand, data teams have a habit of operating in silos and so are unaware of what the business needs and why. On the other, they often become so bogged down in introducing large technology deployments and creating dashboards that “they forget their objectives and what they’re trying to do”.
“My best advice is to make sure you don’t become locked in an ivory tower. It’s hard sometimes to communicate with the business, but you’re part of it, not separate superstars, so make the most of it. It makes life much more interesting,” Broadhead concludes.