Data analytics — what’s next?
The big question this month asks four industry experts: What is the next big enterprise trend in data and analytics?
Data literacy
For employees across diverse industries, the ability to read and interpret data has become an essential part of their role. Whether it’s retail, manufacturing, financial services or other key sectors, data literacy is an increasingly vital skill. Digital skills are now part of the entry requirements for two-thirds of UK occupations, and these occupations account for 82% of online job vacancies.
Once siloed within data analytics and business intelligence teams, there is now an opportunity for employees across an organisation, whether in the finance, legal, HR or marketing department, to work with dashboards and make data-driven decisions that impact the future of their business.
However new research from Salesforce and IDC finds that one in six UK workers have low or no digital skills. The digital skills gap is a major concern for businesses up and down the country and needs to be addressed urgently.
This has a severe impact on our economy — costing as much as £2 billion annually. With employees unable to understand or use data, businesses will have lower levels of productivity and efficiency and will fail to fully adapt and digitally transform their operational models.
Organisations know that data is integral to making important and informed business decisions. Despite this, many companies have yet to prioritise upskilling their workforce or developing a data-first culture. However they do this, it is imperative that data literacy training isn’t treated as simply a box-ticking exercise. Arguably, this should be one of the first things that employees are taught when they join a company, and should be a continued investment throughout their development.
Ultimately, businesses will play a pivotal role in levelling up the UK workforce. Tableau offers data literacy e-learning courses to help anyone learn the foundational data skills they need to meet the evolving needs of employers. The UK’s GDP is driven by the digital economy, and one in three UK businesses expect to create new digital services. Therefore, providing those in employment and those still in education with the tools they need to see and understand data is more crucial than ever.
Customer Experience collaboration
We recently commissioned research among decision-makers in large businesses to determine what they view as the next enterprise trend in data and analytics. While the research report is yet to be released, preliminary results indicate a strong propensity towards increased customer experience (CX) collaboration, driven by the pandemic.
The respondents agreed that a great CX team is essential to their company differentiating itself from the competition. Covid-19 elevated the digital customer journey as a determining success factor. Yet, while the pandemic spurred the amplified focus on the discipline, the respondents also found that it has complicated collaboration, citing the lack of face time and busy schedules as major issues.
To improve CX interactions at their organisations, enterprise decision-makers put collaboration technology and more personalised, timely, and coherent communication at the top of their wish lists. Not surprising considering that more than three quarters have worked on a CX project in the last 12 months, most of whom have collaborated with colleagues in other departments. However, this collaboration appears to occur on an ad hoc, informal basis.
When it comes to internal CX collaboration, the respondents mentioned several stumbling blocks they have to contend with. These included lack of time, internal politics, people protecting their ‘turf’, disjointed data, and poor internal communication. Seemingly, many organisations find the optimisation of their CX resources challenging. Just under half of them also say that customer data captured in the CRM isn’t used appropriately.
Perhaps the most surprising aspect of our research is that half of the respondents keep their views on topics outside their departments to themselves. This tendency contrasts with their seniority and the fact that many acknowledge that businesses fail because they aren’t ‘joined up’ enough — many indicated that collaboration simply isn’t part of their company culture.
To improve CX interactions at their organisations, enterprise decision-makers put collaboration technology and more personalised, timely, and coherent communication at the top of their wish lists.
Empowering Developers
Enterprises have taken huge strides around making their applications ‘cloud-native’ — taking advantage of approaches like containerized microservices and new orchestration tools like Kubernetes, they can scale up their applications to meet global demand and move those applications where they want to run them, in the cloud or on-premise. The next wave is in containerized data services and bringing everything into a common control plane.
The challenge here is how to standardize and scale up around data alongside the applications that create all this information. Open source projects like Apache Cassandra and Apache Pulsar already support some of the world’s largest data deployments, but these components are often disconnected from each other. Getting a complete open data stack in place that can scale around the whole process. From where data is initially created, through to analytics and eventually where data is at rest. Ease of use and fewer trade offs will help more companies improve how they use data.
Alongside this, those involved in data and analytics projects have to start thinking about new ways of integrating into the total stack. Developers don’t want to spend precious time evaluating database technology, so they pick the easiest service to implement — even if that decision turns out to be wrong over time. Instead, we have to bring the right database services to the developers and make it easier for them to consume those services over time without long term technical debt accumulation.
Developers think in terms of API access, so supporting standardised approaches through a data gateway service will help them get implemented faster. Open source projects like Stargate make this process around integrating data and applications easier for developers as well as for any DevOps or IT infrastructure team that has to support them.
There is still a lot of discussion — and a lot of hype — around AI and machine learning (ML). The next year will see more mainstream enterprises look at how to integrate across areas like data pipelines for AI and ML with their existing database deployments. Uber’s Michaelangelo is a great example of this — they have a large Apache Cassandra instance which they use for fast lane AI workloads. More companies will take up this approach so they can use their database clusters as feature stores for AI.
So what is the biggest trend in enterprises around data and analytics? It will be how they can turn the hype around being data-driven into operational reality. That means optimising how to run these services in production, and that involves getting all the different pieces working together as part of an open data stack.
Data accessibility
A key challenge currently facing organisations is data accessibility. Most by now understand that raw data by itself isn’t enough to transform themselves into an entirely data-driven entity.
Data becomes valuable only once it’s been transformed into something understandable which an organisation can then use in its operations and other processes. Well-established data visualisation practices, frameworks, practises and tools can discover and extract this value. Businesses should revamp their data systems using logic-based features such as nomenclature, taxonomy, and funnels.
Employees will no longer be required to analyse data: instead, their focus will shift to using their creativity and expertise to realise any new possibilities unearthed by the automated analysis. Explaining business decisions with relevant supporting data insights will become the norm.
As data analytics becomes normalised and commonplace, organisations should build a Data Analytics/Business Intelligence team that can communicate effectively with the business and also frame their thinking in terms of the priorities of the business. Empathy and understanding others are key to this communication, which is why setting up this time requires different hiring decisions and training processes. A Business Intelligence division cannot solely consist of “data geeks” anymore.
Turnaround times for data analysis are also crucial. The decision-making process is accelerating. If it takes too long to unearth insight and trends from a body of data, analytics will be simply left out of the decision process altogether.
In order to answer more complex and infrequent questions, data teams must prioritise improving data accessibility. This means building systems that automatically turn information into easily digestible insights that can be accessed by all employees working in relevant fields. However, if they fail to automate these processes with a systematic approach, data accessibility suffers. The result? analysts will drown in a never-ending stream of one-off custom requests.
Ultimately, the direction of travel is to phase out the dedicated data analyst role by giving employees the tools that allow them to directly access the data insights that can support their day-to-day decision making.
This means processing data to turn it into actionable insight for knowledge workers, and then exposing it through well-designed, easy-to understand employee experiences. With the proliferation of simple data visualisation tools plus the ongoing development of machine learning, expect to see significant breakthroughs in this process over the next few years.
Data becomes valuable only once it’s been transformed into something understandable which an organisation can then use in its operations and other processes.
