Bridging the business intelligence and analytics gaps

Business intelligence and analytics continue to challenge organisations of all sizes.

There is a huge disparity between the data collection capacities of today’s businesses and their analytics capabilities.

In an age when information is produced in massive amounts, some companies are drowning in spreadsheets and lack data automation altogether. Meanwhile, others have invested millions of pounds in every analytics tool available.

Despite continued investment in the area, companies have been challenged to find the right way to approach business intelligence (BI) and analytics.

In the past, BI started off with huge and expensive installations to gather data together in one place using data warehouses.

This proved useful for creating a single source of truth, but it left firms slow to react to changing business requirements.

The alternative was individuals using their own productivity tools to work with report data and then visualise it.

>See also: 5 myths about business intelligence

Over the years, the BI pendulum has swung from a centrally managed IT-based, governed approach to an approach where end users are analysing their own, siloed sources of data.

Getting to that middle ground – where data is governed and trusted, and individuals can work with it in their own ways – should be the goal.

As they embark on this brave new world of BI and analytics, organisations crave consistency.

However, many lack the right technology and processes to achieve this. The muddled nature of approaches to deploying analytics therefore sees many firms fail when implementing large scale projects.

Part of the problem centres around being able to view data in a useful way.

It is all too common for people from different departments to turn up at meetings with multiple answers to the same question.

This leaves others within the business unable to figure out the truth. At best, one trusted version of data becomes gospel. At worst, business leaders and CEOs have to make decisions, based on conflicting data, which affect the future of their businesses.

Data discovery

Adding to complexity, there has been an influx of data discovery tools deployed independently from IT.

Once these tools get a foothold in one department, firms then try to implement a centrally run approach to data discovery that enables them to see what’s actually going on in the whole business.

This is made more difficult by each department using big apps such as Salesforce, which can create silos within the organisation.

It means departments don’t talk to each other and, again, it leaves leadership teams trying to comprehend multiple figures covering the same thing.

>See also: The central intelligence atlas: how BI is going visual

The optimum approach is one that blends the centralised and decentralised.

As part of this, the right technology can help. After all, analytics is a means to an end: people invest in BI and analytics to have access to information that helps them make better decisions.

Networking these sources of data can help in the process.

By virtualising access to information and making it easier to combine data sources, multiple departments can take part in data analysis around the same sets of central information.

If a member of the department needs to add his/her own local data, this can be done as part of their virtual space rather than relying on central IT to add or move physical data.

The central IT team can own and manage the data, and everyone can get access to it, too.

The politics of business 

Of course, internal politics also play a part in failed BI projects.

Overcoming this requires data silos between departments to be broken down. At the same time, it’s important to get the balance right, so each department is still in control of its own performance.

Therefore, companies need to create the right metrics that they can track over time.

They should create an overall key performance indicator (KPI) for the business, as well as sets of key value indicators (KVIs) and drivers assigned to individual departments to show how each delivers value.

This can be achieved using analytics tools to create the right dashboards that can show the contribution each particular role makes to the organisation.

>See also: How to bridge the data divide

The KPI can sum up a business-critical process – for example, customer acquisition or profitability. The KVIs are then provided for each team involved.

In this example, the marketing department might track web traffic and marketing automation campaigns, while the sales team concentrates on pipeline volume and time to close deals.

Taken together, these KVIs add up to that overall business goal being improved.

To most firms, this is a new way of working, so it is vital that everyone is on board. The best thing to do is appoint a visionary such as a ”chief data officer” (CDO) to oversee the move to efficient and integrated BI.

A CDO can also be an arbitrator who can sit between groups to prevent departments fighting over data.

He or she can emphasise the benefits of sharing information between departments, which itself creates transparency. Once people in the organisation are aware of this, it breaks down the barriers and, in turn, benefits the entire business.

BI and analytics dashboards – where it is easy to consume data – are ideal for this purpose.

For example, a marketing dashboard can be given to sales, showing how opportunities are created and explaining the reasons why a particular method is used.

It enables sales to see what marketing is doing and this can be discussed with other departments to make sure it is working for everyone.

In addition, sharing the analytics between departments that never normally talk to each other encourages them to collaborate.

It just requires a different organisational mindset – and this can be driven by the CDO.

However, this should be about more than just sending spreadsheets or PDFs to each other. Instead, the analytics results can be networked together and shared, real-time, so that everyone can see how these results are put together.

Staging the approach

When done in the right way, BI and analytics are of great benefit to the business.

>See also: From global to local: how enterprises can embrace truly ‘pervasive BI’

According to Nucleus Research, every dollar invested in analytics should return $13.11.

To achieve this level of return when carrying out BI projects, the best idea is to implement a staged approach using agile methodology, breaking things down into manageable chunks.

Solving today’s BI and analytics challenges is a combination of people, processes and technology.

In other words, BI and analytics can improve the culture of decision making and data transparency, if you have the right people in place to manage it.

In the end, the benefits of BI and analytics are both tangible and intangible.

Analytics should deliver hard savings in terms of more efficiency and productivity, as well as revenue gains from data-driven marketing and sales campaigns.

Other benefits can be harder to measure. The transparency gained when departments share their information can improve the whole culture of the organisation, which itself leads to better results.

Looking ahead, many companies want to pool even more information into data lakes that are based on technologies such as Hadoop.

However, it’s important for organisations to ensure that they can turn these new sources of data into valuable insights.

>See also: The data of design: how analytics opens new doors for embedded system design

Whether it’s information from a long-standing ERP system or a new data lake, all of the data must be made available to those who need it, in ways that they can work with.

By looking at the gaps between centrally-driven and end user-driven analytics, company IT teams can help ensure that everyone makes better decisions.

Sourced by Richard Neale, EMEA marketing director at Birst

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Nick Ismail

Nick Ismail is a former editor for Information Age (from 2018 to 2022) before moving on to become Global Head of Brand Journalism at HCLTech. He has a particular interest in smart technologies, AI and...

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