More and more companies are finding their way to the establishment of an analytics practice that analyses data quicker, better, and faster across the enterprise than could have been conceived of when the journey first started.

The enterprise journey from the older business intelligence departments that sprang up from the 1960s to the more modern analytics teams that work across an organisation has been a steadily increasing trend from centralised control to decentralised empowerment. Now, the everyman ‘line of business’ analyst can combine their detailed knowledge of specific business challenges with access to company data to deliver more comprehensive analyses.

Now, cloud services have really broken down internal physical and organisational barriers, and made it easier for any kind or size of business to streamline and open up enterprise data, whilst still retaining control of access rights and security. What’s more, complex analytics challenges that used to require statistics grads and quantitative analysts to execute complicated predictive modelling and company reporting are being solved by line of business analysts. Solving these challenges no longer relies on either knowledge of coding (leaving them within the domain of IT users), or are so knowledge-specific that only PHDs can understand the statistical requirements.

[easy-tweet tweet=”‘Data democratisation’ has been enabled by the rise of big data analytical solutions” hashtags=”Data, Analytics”]

‘Data democratisation’ has been enabled by the rise of big data analytical solutions, from tools to whole platforms, and made sophisticated analytics accessible to the average person within business – those ‘line of business’ analysts who most likely wouldn’t consider themselves an analyst, and wouldn’t have it on their job description or title. Nonetheless, regular business people across every department need to answer business questions, plan for the future, and optimise the activities. To do all of these things, they need good data to ensure they aren’t making poor assumptions that may render their plans suboptimal.

Working smarter not harder

So what enterprises have experienced over 60 years of increasingly rapid data explosion, experimentation, and harnessing, is a whole new way of making quantifiably better business decisions. At the same time, the ability to derive benefits based on data have cascaded down from siloed pockets of ability or knowledge within IT, or driven sometimes by enthusiastic supporters in particular departments like marketing or finance, into every department.

Now, leading businesses allow employees who need to access data as part of their role to become their own analyst, rather than funnelling reporting and query requests to a centralised but non-business expert function. But for governance and security, a central resource is often maintained to act as a guardian of data quality and ensure that the business uses its data appropriately and effectively. With all users familiar with the chosen analytics platform, the business is able to allow data to be accessed seamlessly from end to end of the business, with no artificial breaks for fresh data prepping, cleansing, or reworking that bedevil the average enterprise.

With this arrangement, an enterprise is able to unleash four things:

Accessible data for all who need it

  • It’s no use if Finance needs to know how effectively Marketing budgets are being spent if neither team is using the same metrics or underlying data. The whole business should be working to common standards and from a shared view of the truth.

Faster time to decisions

  • With data from all teams able to be interrogated and shared, there’s no more business silos, requests for information, and back and forth. Business people make the decisions they need to with the data they have permission to access. It can be that simple.

More advanced business benefits

  • Ideally, all levels of employees should be able to learn not only from data analysts, but from each other as their confidence and use of data analytics grows and a culture of fact-based decision-making matures. Then, instead of creating a dashboard or a report once or twice, they use analytics to solve much bigger business challenges. In fact, every employee has it within them to be a deliverer of enhanced business value by discovering their own marginal (or more major) gains in every corner of the business’ activity.

Freeing up IT

  • There’s no longer a good reason for IT to own all the data, and be required to run reports at the behest of other departments. It’s an inefficient use of resources and one that no longer makes sense in a data democratised world.

There are analytic platforms that empower analysts and business analysts to more easily consume data and make more informed business decisions by avoiding the bottlenecks of a traditional centralised data repository. As with all cloud-based solutions they are designed to meet expectations for flexibility and scalability, without sacrificing security or performance. Simply put, a decent analytics platform should make it easier, quicker and safer to deploy analytics across an organisation.

From A to Z (Analyst to Zettabytes of data!)

Democratic analytic solutions that empower the regular business user, engender a data culture. Where possible there should be a Chief Data Officer (CDO) or other authority steering the business with best practices, whilst allowing decision-makers the freedom to experiment with their data. These are the core components of a smarter analytics programme that holds the potential to invigorate every element of the business.

Adding a cloud element may allow a business to break down any remaining silos and mature the data culture at speed, providing analysts and business analysts with access to business-critical insights to make more informed, data-driven decisions in less time. When analysts can build repeatable workflows that prep, blend and analyse data, they can publish these to decision makers. Decision makers can browse and customise and execute workflows on demand, without interrupting the analyst who originally created it – once again saving time and effort.