Business Analytics: Warning Signs To Look Out For And SolutionsIn recent years, business analytics has become more sophisticated, more specialised, and can be an expensive exercise if mismanaged

Business analytics has been a part of Australian corporate culture for decades. In recent years, it has become more sophisticated, more specialised, and can be an expensive exercise if mismanaged. There has also been a significant push for artificial intelligence and 'smart' analytics, with big businesses investing millions of dollars into probing schemes. However, are businesses paying enough attention to the analytics warning signs to ensure that these projects are set up for success?

It is crucial to look carefully at how and why you use analytics to be able to see any red flags. In many cases, businesses may not even be aware that they are creating problems. To discuss in detail, we must consider some of the common problems companies encounter, and the solutions to overcome them.

Money Is Going In, But Nothing Changes

A typical failure in business analytics is where things never actually change. A business can pump money into programs and into hiring data analysts and data scientists, but the work they do needs active implementation.

When analysts deliver results but reap no change, it is a clear sign that a business detaches itself too far from its analytics. A clear warning sign to look out for is where an analytics centre is wholly isolated from business processes.

The Solution

解决这个问题的方案相对straightforward. A business should integrate some analytics into its core. That means teams can easily collaborate to reach mutual conclusions. Doing so, not only helps data to translate better across the board, but it also means more meaningful and useful, analytics programs for the future. Analytics use cases should be prioritised based on feasibility and impact on the business while building a data-driven culture should be a top priority.

Limited Scope Equals Limited Results

One of the most evident warning signs rests in how limited a business' scope is from the get-go. Many businesses fail to scale analytics programs properly, such as to plan for detailed scenarios and instead create vague, generalised plans. It is a common misconception that an analytics program begins and ends once a project is complete. In many instances, analytics tweaking never truly ends, it keeps evolving to support the business as it changes.

If a business analytics plan is non-specific, or perhaps even too specialised, it is doomed to return limited results. Inadequate scope is easy to spot; however, it is a common mistake many make when getting started. A genuinely effective analytics plan must make room for advanced case studies, those likely to occur in the future, not just in the present.

The Solution

A remedy to this scenario is easy to find but can take time and resources to correct. Analytics programs must consider theoretical problems. Just as any good business plan will discuss potential challenges and pitfalls, so must an analytics plan. Workable analytics, those which provide valuable, actionable data, require extensive planning.

Ultimately, initiating an analytics piece of work without looking at the process carefully will result in money lost on limited data. It is a sure-fire way to find out what you already know.

The Executive Team Doesn't Have a Clear Vision For Analytics Programs

This is especially evident with the onset of advanced analytics. It's important to not just jump on the bandwagon and launch headfirst into an artificial intelligence or machine learning project without fully understanding how it fits within the wider data and analytics strategy for the business as a whole.

The Solution

This can often stem from a lack of understanding between traditional analytics (descriptive) and more advanced analytics (prescriptive, and predictive). Whoever is tasked with leading data and analytics in the business needs to educate leaders in the business of where more advanced analytics fits into the bigger picture and what problems it addresses before investing in building skills and deliverables.

Business Analytics Roles Are Poorly Defined

上述分析假定其他征兆e are clearly defined and agreed roles for data analysts and specialists on board a program. In this scenario, however, there are no specialists. Some companies will assume that no specialist data or analytics skill set is required and that they can manage inhouse with existing teams in addition to their other responsibilities.

That can lead to frustrated business users, time and cost inefficiencies. Fortunately, this is a simple one to correct.

The Solution

As part of the business scoping phase of any analytics projects, roles and responsibilities should be clearly defined - engaging both technical, sponsor and business process owners. This includes any internal and external parties involved. Where skills gaps or resource constraints are evident, specialist data and analytics consulting partners could be engaged to complement in-house resource or to deliver an initial project to set benchmarks. Inhouse teams can then be trained to deliver post the initial project.

A useful takeaway from business analytics failures is that without them, we cannot learn and adapt. It is becoming more apparent than ever why companies are failing to deliver the results they desire from analytics. However, at the same time, business executives must be ready and willing to understand and appreciate analytics at more than just a base level. This way, analytics warning signs can be handled at the point of realisation, and not once data is delivered.