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How to leverage data for better decision-making

Cross Roads

By Ian Lavis on behalf of Praxity Global Alliance

Learning how to leverage data is critical to operational efficiency but you need to tread carefully to maximise ROI. 

In the ‘age of information’, organisations seek to analyse increasing amounts of data in ever more complex fields to improve efficiencies and gain a leading edge.

However, the sheer amount of information now being produced leaves many organisations struggling to get value from it, especially as they grow and diversify.

“Data has exploded,” says Mark Richards, management consultant and business analytics leader at Plante Moran, a participant firm in Praxity Global Alliance. “We are drowning in data and organisations don’t know what to do with it.”

It’s not just a problem of data overload. Failing to understand how to manage data can lead to ill-informed decisions based on poor analysis.

Overcoming the data challenge

To overcome the data challenge, organisations need to ensure they have every aspect of business analytics covered, from technology to training.

“Business analytics is the next competitive breakthrough following business automation, heralding increases in efficiency and effectiveness, but what if the data that feeds those analytics isn’t correct?” Mark says.

“The risk of making the wrong decision based off bad or unmanaged information may be as high, or higher, than the risk of making a decision based on experience alone. Many analytics programmes are implemented without an understanding of this journey or the maturity that’s necessary to succeed,” he adds.

As the business analytics leader for Plante Moran’s Rocky Mountains region, Mark helps clients “understand, develop, embrace, and execute their vision for data analytics and reporting”. This includes business intelligence (dashboards and KPI visualisations), data management (integrating, architecting, cleansing, mastering, and staging data for reporting), and using technology to make budgeting and forecasting more efficient.

The first step, he says, is to understand where your organization sits on the “data journey”, and the risks and rewards at each step.

Mark and his business analytics team have identified five distinct levels on this data journey, as illustrated in the table below:

Source: Plante Moran

The vast majority of organisations are between levels one and two. The biggest challenge facing many firms is the move up to level three but once this level is achieved, the ROI can be significant.

Let’s consider each level in turn, the benefits and challenges, and how to move up to the next level:

Level 1: Opportunistic analytics

You can’t understand what’s going on in your business unless you do some form of data analysis. For some small businesses, this can mean little more than entering information into an Excel spreadsheet and analysing it.


Opportunistic analytics requires very little investment and is easy to implement.


The main problem is there is very little quality control and the risk of conflicting or misleading information is significant. Data is often manually keyed or updated which can be very labour intensive and prone to error. There are also security issues due to the fact data can be stored solely on an individual’s desktop.

Mark explains: “Output is often static in nature and distribution is limited — shared via email or posted to a server location. Reports generated by different people can often be contradictory based on different data sources, interpretations, and errors, and can be difficult to reproduce. Maturity level is low, but reports are often embraced enthusiastically merely for the new perspective they present.”

What to do next

Plante Moran’s data analytics expert recommends taking an inventory of all the solo projects that exist, and identifying consistent requirements across each. This will allow your organization to define and quantify the latent demand for analytics and initiate development of a more uniform approach to investment and outputs.

Level 2. Siloed analytics

These analytics programmes are aligned with, and segregated by, individual departments. While an organisation may have invested heavily in analytics initiatives, the approaches, capacity, capabilities and toolsets may vary considerably between departments.


Reporting business information is more advanced than opportunistic analytics and individual departments have more useful information to base decisions upon. Investment costs are modest.


Adoption of best practices is low to medium. Information is segregated and there is little to no knowledge sharing. While costs are relatively low, it can be wasteful due to duplication in different departments.

What to do next

Collaboration is key. Discussions on strategy should include representatives from all departments investing in analytics and should focus on development of:

  • An analytics vision, success criteria, roadmap, and gap assessment
  • The creation of “art of the possible” visualization scenarios
  • Identification of key standards and business rules
  • Evaluation of tools and resources able to integrate and convey information quickly and effectively

Level 3: Enterprise analytics

A more holistic approach, enterprise analytics involves sharing information across an enterprise, not just between individual departments.


A data-driven organisation allows executives to combine data elements and obtain more valuable insights. This will provide a more accurate picture of performance and operational health.

“Data must be cleansed, standardized, mastered and ‘architected’ to enable analytics discovery. Process and collaboration become especially important here,” Mark says. As does security and data governance as centralised reporting repositories are built.


Unless managers prioritise, enterprise-level investments can quickly escalate in scope and cost, putting pressure on resources.

What do to next

Organisations are advised to adopt a phasing approach to enterprise-wide analytics. Mark recommends investment in training, data management, and data governance to establish the foundation for a “scalable analytics environment” with frequent, manageable delivery targets (usually reports or dashboards), and increased adoption and ROI.

But he warns against getting carried away with beautiful dashboards and user interfaces at the expense of the actual management of the data, adding: “Nowadays, you can have stunning visualisations but the downside is people focus on the visualisations rather than the key indicators and what they can get from the data.”

Levels 4 and 5: Predictive and prescriptive analytics

This is where statistical modelling, forecasting, machine learning and artificial intelligence (AI) come to the fore, allowing users to run through multiple “what if” scenarios.


The main advantage is your analytics evolves from trailing to leading. It will impact innovation and market differentiation, and support operational effectiveness.


As data analytics becomes more advanced, the required resource capabilities, models, processes, and tools become more complex and specialized. Governance, controls and security become even more important to improve data handling and protection.

What to do next

To maximise the benefits of predictive and prescriptive analytics, organisations need to ensure there is a very strong relationship between those with a mastery of the data and those with a mastery of the business processes, operations, and competitive marketplace. This requires effective training and communication across an organisation.

Wherever your organisation sits on the business analytics journey, there are actions you can take now for increased operational efficiency and better decision-making.

“Finding the right level of investment for your company and identifying areas of improvement are key.” Mark argues, adding: “It’s about looking at what data you have and what you can do with it. It’s the thought process, or what we call ‘the art of the possible’.”

With big data fast-becoming the norm, and technological advances like machine-based learning and AI making huge differences to the way we process information, the need to leverage your data to gain leading edge is greater than ever. Praxity participant firms that specialise in data analytics can help lead the way.