Self-service analytics is fairly easy to define, and its benefits fairly easy to summarise. As an increasingly important trend in the field of Business Intelligence (BI), it makes sophisticated, data-driven insights highly accessible to anyone within an organisation who might make use of them.
In a sense, it democratises analytics: the advantages of technical expertise are no longer the sole province of statisticians, data scientists, and other technical experts. With these insights widely available, employees can theoretically operate with greater flexibility, autonomy, and efficiency – focusing on the things that require more thought and creativity, and automating the things that don’t. Manually building, updating, interpreting and maintaining databases becomes a thing of the past.
But if self-service analytics seeks to make analytics simple, implementing it is anything but. Becoming data-driven is, for many organisations, a serious aspiration. Making it a reality is rather more complex: a report from New Vantage Partners suggests that, while 85 per cent of firms have launched programs to create this all-important data-driven culture, only 37 per cent have been successful so far. It’s unsurprising: incorporating self-service analytics isn’t always as simple as it might seem.
Any adoption of new technology requires a careful planning, consultation, and setup process to be successful: it must be comprehensive without being too time-consuming, and designed to meet the specific goals of your business end-users. Accordingly, there’s no one-size-fits-all approach: each business will need to consider its specific technological, operational and commercial requirements before they begin.
That said, there are some common rules that – if followed scrupulously – can expedite the process. Here are just three of the most essential.
1. Define your business requirements
Adopting new technology for the sake of adopting new technology benefits nobody. Anything you implement – from marketing automation platforms to software development tools – should be strictly aligned with the needs of your business and its constituent departments.
So, when it comes to self-service analytics, any implementation should be driven from the boardroom down, involving staff at every level in the process. Executives and senior managers must fully buy into the process of becoming a data-driven organisation, and should take an active role in asking users what they require from this technology.
It’s unwise to go too big, too early: at first, using BI for more basic analysis with readily-understood metrics is probably wise. From there, you can build and adjust according to various departments’ objectives and expectations. Colleagues won’t have instant expertise in how best to use self-service analytics, so it’s important to host regular training sessions: those who have the fullest understanding and experience of the technology – the ‘super users’ – can help guide and instruct those who are struggling.
2. Collaborate and integrate
When your self-service analytics rollout is underway, it’s time to start thinking about how to bring together the disparate teams that are using this technology. If they’re using it in isolation, they’re not taking full advantage of it; creating data silos leads to awkward situations where the left hand doesn’t know what the right hand is doing, which in turn leads to unhappy customers. When information isn’t shared effectively across teams, customer experience becomes fragmented, disjointed, and less professional. Work to integrate your analytical capabilities and everyone will benefit.
Sales and marketing, for example, should naturally share information: the former needs to know that the latter is generating enough quality leads, and the latter needs to know that the former is making the most of them. In this case, collaborating is not simply natural, but almost mandatory.
But it’s necessary to investigate less intuitive integrations too. Self-service analytics can allow you to create relationships between departments that aren’t necessarily the most natural fit. The sales and marketing teams can share data with the customer service team – benefitting from an understanding of how the support team are treating their clients. Customer service, for their part, will have deeper knowledge of what clients have bought and what they expect.
Connections with HR, finance, and other teams are also possible, but arguably the most important point of contact is the IT team. No data-driven technology implementation in any department will go as smoothly as it can if the IT team is kept at arm’s length. They know how to develop best practice policies and tackle any questions around security or support.
This is not to say they should be bothered with minor queries or distracted from their daily duties – but if they are not at the heart of your rollout, the rollout is less likely to be a success.
3. Create and implement a data governance policy
Make sure you implement your self-service analytics system correctly, update it regularly, and look for new ways to take advantage of it, and it will serve you well.
Fostering departmental collaboration within your data-driven organisation is a good thing, but it can only be achieved with a clearly defined data governance policy. Access to information is as important as the information itself.
Accordingly, self-service analytics should only be implemented with full transparency in mind. A centralised data management system should offer unfettered access to a unified, constantly-updated source of verifiable information. A degree of diligence and vigilance on your part is necessary: if you don’t introduce regular cleansing practices to ensure that all information is relevant and up to date, every department in the business will be working from bad data.
Indeed, diligence and vigilance should be your company’s watchwords when it comes to self-service analytics. Integrating this technology into your operations can significantly boost the intelligence of your company’s teams. It offers unparalleled insight into customer behaviour – providing the opportunity to address or correct issues before they arise; to use their buying history to predict their future behaviour; and to devise sales and marketing strategies that incorporate their proven preferences, rather than merely guessing at them.
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