The development of an analytical culture is essential for businesses to stay ahead of their competitors.
Gartner’s release of its Top 10 Strategic Technology Trends for 2015 made for interesting food for thought, and at Information Builders got us thinking about how we see the data analytics industry panning out this year.
Gartner’s top 10 tech trends reflect a rapidly evolving world in which greater volumes of data and demand for analytics are becoming increasingly pervasive, not only in the workforce but also in machines. Not only is business analytics for everyone, but the hard fact is … it’s for everything; just consider the billions of devices in the Internet of Things. I believe this will become increasingly prevalent in 2015 as the amount of data at people’s fingertips continues to grow exponentially.
This in itself is an important message for today’s executives to take on board, as the amount of data they have to deal with demands an analytical business culture able to stay ahead of the competition.
With this in mind, the areas that we think will be key in the forthcoming year are:
InfoApps and self-service (advanced, pervasive analytics delivered through apps - for everyone)
The digital skills gap
Master data management
InfoApps and self-service
With the growth in data and consumerisation of IT, everyone will have the need for business analytics in order to make smarter decisions. Everyone is a decision-maker in one way or another in their role within a company and, therefore, should have access to data essential to their jobs, and the ability to analyse and make decisions from it in order to be more productive on a daily basis.
Today business intelligence (BI) has a less than 30% adoption rate in the enterprise (source: Gartner). Unfortunately, it is still the domain of professional analysts who use complex tools and spend most of their time analysing data, and managers who view reports and dashboards.
Often, the remainder of operational employees - and beyond to customers and partners - still do not have direct access to information to help them make better decisions.
A key step in ensuring a high adoption rate is realising that one size does not fit all when it comes to BI and analytics. It is critical that various users are served by the right approach - analysts with tools, front-line workers with handy apps.
Gartner also supports this, stating that one of the keys to achieving pervasiveness is a new way of delivering this intelligence - through apps. Making the analytics invisible by embedding them in an easy-to-use app is the right approach to encourage pervasive BI and analytics.
An app delivers users all the information that they need to make informed decisions, without doing analytics, but still gives them flexibility and choice in how to use data, as well as being able to drill down into the information.
For the organisation, the benefit of pervasive BI and analytics is a cultural transformation where strategy and operations are completely aligned through a common system of fact-based decision-making.
The digital skills gap
However, despite the benefits of driving BI and analytics use across an organisation, the role of the analyst and data scientist is still of great importance. There is high demand for these kinds of positions but unfortunately not the supply to meet it, leading to an Australia-wide digital skills gap.
According to an Economist Intelligence Unit survey published in July, 82% of marketers state that career skills have changed, with 37% indicating they don’t have the skills required to analyse and understand the vast amounts of data available to them.
In the next year we’re going to see the role of the chief data officer (CDO) become essential as data is recognised as the most important asset in enterprise today. As more businesses rely on data, so too will they rely on the CDO, in particular for ways to monetise this data.
We’ll also see the chief analytics officer (CAO) role grow in response to the need to analyse trends. The challenge here is to find people with the business acumen, not just the technical skills.
As with any labour problem, the solution lies in technology and in this case we’re talking about machine learning. Looking back, Deloitte made some interesting points here in its Analytics Trends 2014, that managers had previously steered clear of machine learning for decision-making as there was no hypothesis or human explanation behind it.
However, now, data projects are often moving too quickly for traditional hypothesis-driven analytics. This explains why businesses are embracing machine learning to help them deal with the large volumes of multidimensional data they have access to.
Data these days can have many variants - age, education, income, frequency of purchase and so on. It’s difficult to do this kind of visualisation without the aid of a machine as typically you can only include three variants. By using mathematical techniques, machines can trawl through the data to find patterns for analysts to then look at and discover trends.
This can be translated into business strategy such as determining which consumers should be targeted with which marketing campaign for high ROI on marketing spend.
Master data management
As a result of analysts working with this growing amount of data, another issue that will become more prevalent in 2015 is that of master data management (MDM).
Analysts need to have the freedom to work with this data how they wish, but IT also needs to be able to manage it to ensure that analysts are reaching the same conclusion from the data they are looking at. This becomes complex when multiple sources are being merged and perhaps not appropriately being described in metadata.
We’ve mentioned that data is growing, but it’s not just about the quantity, it’s also about the number of data sources - there are avenues like social media that are providing a wealth of information for businesses now - and analysts want to look at this in real time.
Data governance is key here, to give IT centralised control but analysts the necessary flexibility. It’s also about expanding this capability across the organisation to a self-service model, where any employee can access company data that’s relevant to their role.
Another interesting aspect of this trend is the importance of integration and real-time data processing and analysis.
As more emphasis is put on context-based systems, the depth and breadth of the context is critically contingent on the data collection, data quality and data integration from multiple systems.
Any gaps in those processes make the picture incomplete or fuzzy, thus reducing the value of the context for decision-making. These gaps can also prevent the automation of decision support, which is crucial for intelligent machines.
This also ties into big data. Machine data and unstructured data, like social media data from customers, should be a key consideration for businesses - this can help them grow and can drive revenue.
Whichever way we look at it, it will be interesting to see how businesses deal with the data challenge in 2015. The benefits these trends I’ve outlined can bring are multiple but only when it is available to all business users, not just constrained to analysts or the c-suite.