Big Data analytics is much more than a buzz phrase. It is a critical factor that is increasingly impacting the business landscape. Big Data has not only woven itself into the fabric of 21st century commerce, its importance is expanding and cannot be unstitched.
The tsunami of data is both an exciting and intimidating challenge for today’s business decision makers. Yet it is also an opportunity. Analysts and commentators are unanimous in their verdict that the appropriate deployment of data analytics will ensure businesses can boost innovation. This in turn will allow those companies to improve their commercial reach to not only new or existing, but as yet unrecognised, market opportunities.
According to Deloitte, more than 40% of Australian private companies will invest in business intelligence / data analytics in 2019. Data analytics will not only enable more effective marketing of current products but has the potential to unleash new business horizons through the understanding and creation of entirely new products and services .As a result, properly implemented data analytics will augment human capabilities, which will deliver measurable gains in employee productivity. Staff will be freed up to tackle more rewarding and higher-value tasks.
The consensus: it is the CFO level that will be best equipped to achieve these goals by taking ownership of an organisation’s data projects. While there is the strong opportunity to lead the company commitment towards this brave new world, today’s CFO needs to tread carefully on this unfolding data implementation landscape.
Data projects come to life because a business issue needs to be addressed. The starting point is to ask the right business questions. These should be driven by the overall objectives of the company. Next will be the task of addressing key metrics for key people in the business. With that blueprint in place, a more focused search can be undertaken for the most relevant data and IT environment that will satisfy the needs of the data project.
The finance department vision for 2020 and beyond has been transformed. The CFO leadership role has evolved into that of principal decision-maker and the guardian responsible for future proofing the organisation’s success. Traditionally the finance team interest was centred on accounting but is now being re-directed towards strategic financial management augmented by analytics. Today those same teams need to embrace the new holistic business model. That requires translating data insights into decisions that add value and equip the company to fully grasp tomorrow’s business opportunities.
The need to set out clearly defined benefits
Recruiting or training up the right talent
Overcoming the existing and inevitable functional silos
Today’s CFO needs some stars to guide a best-practice approach for implementing a big data analytics project. Failure to capture, analyse, share, and act on analytics’ powerful offering is an unacceptable risk for future business success.
The incumbent CFO, whether in a large or small organisation, will face the common organisational obstacles to data implementation. Those obstacles may well have become ingrained over time, and therefore taken on a traditional pride of place in a company’s culture.
Disrupting and unstitching – in the most effective manner – these organisational obstacles will require some foresight, sensitivity, and careful footwork by the CFO.
Identifying the critical business issue
Identifying the data that needs analysing
Ideally the data team should comprise business and technical people
Modelling and testing
Challenge hypothesis with clean data and experiment with different data sources. Then test that model with “what if” scenarios
Receive sign off from senior executives and embed the change management philosophy into the systems and processes
This transformation offers an important opportunity for CFOs to drive business performance. But doing so demands that C-suite executives get a consistent, 360-degree view of metrics and a cohesive set of analytics to make data-based decisions. As the data analytics transformation increasingly enables cross-organisational transparency and data sharing, it empowers the company’s key functional executives to deliver better results by collaborating more effectively.
Data analytics implementation strategy should be determined and accompanied by a roadmap. Companies need to build an enterprise-wide concept of critical data analytics opportunities. Harnessing superior insights provided by data analytics has the potential to transform parts of their current business processes.
A strong data capture and governance regime is important because today’s CFO function is likely to be overwhelmed with the number of systems and applications running in the organisation. The volume of data being generated across the enterprise is formidable.
Yet the savvy CFO will instinctively realise that harnessing real facts about the business will facilitate the ability to make the right decisions.
When looking to evolve business intelligence and data analytics capabilities, CFOs will either choose to keep these skillsets in-house, or choose to outsource to an external partner or team. There are advantages and disadvantages in both approaches that must be balanced.
Descriptive analytics answers the question “What has happened?” It supplies the answer by analysing the data coming in real-time and historical data for insights on how to approach the future.
Diagnostic analytics pinpoints the reason why an issue has occurred and can identify previously unseen insight. This type of analytics is characterized by techniques such as drill-down, data discovery, data mining and correlations
Having uncovered the answers, the following phase in utilising the data is predictive analytics. Analysing past data patterns and trends can accurately inform a business about what could happen in the future. This helps in setting realistic goals for the business, effective planning and establishing realistic and attainable expectations.
Now is the time to release the data-backed and data-found factors from the previous steps to create prescriptions for the problems the business faces. Prescriptive analytics delivers the layer that makes manipulating the future that much sounder. It advises on possible outcomes and results in actions that are likely to maximise key business metrics.
Collecting the data is only the first brick in the sea wall of containing, controlling and capturing the real value of the data tsunami. The real value begins when the company shares this knowledge across its employee base. This action unleashes the employees’ ability to use the powerful information data analytics provides.
The critical element is knowing how to claim it, to uncover new insights, and then present those ideas to promote better business decisions. This allows more people within the company – not just the data scientists – to access, analyse, and collaborate on the important data.
The positive outcome is that company culture, process and people will be harmonised with the transformation journey. Therefore, in an enlightened data analytic business:
Advanced analytics can transform existing data into relevant business critical insights.
Advanced analytics is helping companies across many business sectors solve their complex business problems.
The overwhelming number of trends, patterns, and insights hidden in a company’s data are beyond the spectrum of the human eye. A single view of the organisation’s operational heartbeat is invaluable to the company’s decision makers. What CFO would ignore a single source of truth of actionable insights that would help determine the correct decisions.
Used to create campaigns that are designed to generate higher-quality leads. With data, software systems can analyse points and rank them so sales and marketing teams can create more tailored communication to better target the higher “bang for the buck” leads. This will optimise profits by creating quicker sales cycles and more successful upselling opportunities
Such analytics can provide a prediction on the profitability of each client individually or within a segment. These analytics help accounting and underwriting to reduce default risk and losses for business and lenders.
In the same way, analytics can predict the profitability of each customer; it can inform product profitability to help businesses make better decisions on inventory. To help maximise the profit on each product, such analysis can help see which products perform the best and at which price point they will continue to do so.
The software can create cash flow statements to monitor a business’ health. The CFO gets a better understanding how the business is operating. If cash is running low in specific periods, financial analysis indicates which appropriate costs to cut and improve product and customer profitability.
Value-driven analysis can offer insight into “what if” situations to inform better decision-making for the future. It will help see what will change and the effect of a decision before implementation.
Shareholder value analysis assesses a business’ performance by looking at the returns it provides to its shareholders. With this long-term view of decision-making, financial analytics software uses predictive modelling and forecasting to inform immediate decisions for future value.
Despite today’s sophisticated business environment, too often data is incomplete, duplicated, unstructured or outdated. That translates into many organisations struggling to manage, stay compliant, and maintain security over a vital asset. The challenge of demystifying what data business possesses, how it’s classified and how to leverage it becomes an exercise in frustration.
Properly harnessed Business Intelligence will lead to better decisions and improve operational efficiencies. As a result, the company will benefit from an increased competitive advantage. The CFO will have a wide variety of tools, applications and methodologies that enable the collection of data from internal systems and external sources.
Analytics will prepare that data for analysis; develop and run queries against that data; and create reports, dashboards and data visualisations to make the analytical results available to corporate decision-makers, as well as operational workers.Download Your Free Big Data Analytics Guide Now
The CFO should avoid the temptation to ‘boil the ocean’ by trying to bite off too much with the first data analytics engagement. – start small with a targeted business issue, learn as the project moves forward with a plan to ensure the solution can grow as the company grows and will easily adapt to future technologies.
Data analytics implementation is classic change management territory. It demands a systematic approach to what is a transformation of a business’ goals, processes and technologies. The CFO should have a parallel implementation strategy for effecting the change, controlling it and helping the company’s employees adapt to the new environment.
Failure to capture potential value of Big Data
Failure to secure executive sponsorship
Failure to enthuse, galvanise and empower across the organisation
Lack of effective education and communication strategies
Ignoring the absolute need for first class data management
Lack of a good governance regime undermines this valuable asset
GWA Group is a strong performer in the residential and commercial building supplies sector. As a listed ASX 200 company it owns and distributes household name brands including Caroma, Dorf, Fowler, Stylus and Clark as well as leading international brands.
With two distinct business divisions, GWA depends on access to reliable and accurate data for performance reporting, business planning and operational decision-making. That data had been stored in a corporate data warehouse. Despite significant investments in support and upgrades there were persistent concerns about data accuracy and performance issues.
GWA engaged Antares in June 2017 to design and implement a new approach to data management and, in parallel, implement Microsoft Azure’s cloud service.
Antares successfully identified and communicated the root causes of issues and developed a clear action plan – a hybrid solution was chosen with a shift to a structured enterprise data warehouse.
This approach enabled GWA to leverage the flexibility of the cloud, minimise overheads and reduce infrastructure costs, whilst retaining the option to use other Microsoft Azure services such as advanced analytics and machine learning in the future.
Antares applied its enterprise data warehouse framework, which is fully automated and meta driven, allowing the Antares team members to immerse themselves in GWA’s culture and ensure that every deliverable met requirements and expectations. In addition to technical delivery, Antares focused on governance and security.
All Not-For-Profit organisations (NFP’s) are subject to the Federal Government’s clearer focus on funding and evaluating programs based on outcomes. The obligation is ever present – a stronger evidence basis for the effectiveness of their work. For many NFP’s the collection and reporting of meaningful and timely data is essential, but often difficult. This was the reality facing Antares Solutions’ client, one of the oldest NFP’s in Australia, with a legacy of helping those in the community with great needs.
The NFP faced a genuine obstacle in the reporting of a single source of truth across its entire organisation. Decisions makers were unable to respond to customer feedback, identify non-effective business processes and reporting was unnecessarily complicated. Yet the message was clear: irrespective of tool, location or platform, data must be available today, as well as into the future. It must meet the reporting needs demanded by both internal and external stakeholders.
Antares approached the engagement by working closely with internal stakeholders to develop a Data Warehousing / Business Intelligence system that would meet the organisation’s needs and requirements. The key solution elements were:
Understanding the real potential of a company’s Big Data asset is a critical consideration for today’s business leaders. The CFO sits in the best position to capitalise on the value the company’s data analytics investment can deliver. The CFO should be in lock step with the CIO in leading the action on that value.
Approaching the task of analytics implementation, the CFO is entitled to seek help so that the right kind of data analytics solutions is selected that will fit the company’s vision – which should include increasing ROI, reducing operational costs and enhancing service quality.
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