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TYLER KOITKA

TYLER KOITKA

The 5 myths of Analytics you’ve always been told

Analytics is nothing new. It’s an old concept, an offshoot of Business Intelligence (BI), but the variety, evolution, and practical applications of Analytics today will be the predominant tool for business leaders tomorrow. I often consider Analytics as the advanced end state of a mature Business Intelligence system. In recent times, the Marketing Machines have re-labeled BI as “Analytics”. Regardless of what you call it, and whether you are new to BI or have been lucky enough to have worked with it for a while, one ultimate truth remains: the value of a well-designed, implemented and managed Analytics system is invaluable to a business. Sadly, so many Analytics implementations fail and the business ends up with something unusable. IT carries the burden of running a system that should be re-implemented or terminated to save time and money.

Oftentimes, decision-makers are so concerned about these failures that they avoid Analytics altogether or wait until they feel it’s the “right time”, which unfortunately is usually too late. You could be missing out on opportunities you don’t are out there due to a lack of transparency from Analytics.

The root of these concerns, whether they be misconceptions, preconceived notions, or in some cases a lack of understanding, is a major reason why some Analytics projects are doomed to fail. Experience is the best teacher to help you avoid pitfalls and follow the precursors to success.

These misconceptions are pure myths and I discuss here why they are false. After reading this, my hope is you will have restored confidence about starting your Analytics project.

Myth 1: Analytics is not important enough

Analytics is fundamental to any organization’s efforts to analyze business data, identify patterns and implement effective strategies to make better decisions.

It may be hard to believe, but one of the most common reasons any Analytics project fails comes down to a common misconception of its true value to a business. The core purpose of Analytics is to allow you to harness your company’s largest asset – your data.

If implemented correctly, Analytics will transform your data into invaluable insights into all that is happening in the business right now, and what can expect to happen (predictive) in the future based on different scenarios. Analytics is a window into that data, with tools that help with queries, analysis, and the presentation of data in a meaningful way to provide informed, efficient, and profitable business decisions.

Without Analytics, you may be making decisions based on ‘gut feel’ or the way things have always been done, which may not be the best approach in a competitive environment or where continuous improvement is desired. With Analytics leveraging company data to answer your business questions, you can empower departments to make profitable decisions with efficiency. Drive corporate objectives, or undertake bold, transformational strategies that are justified by data.

Myth 2: Analytics is too expensive

Analytics initially required heavy investment in time, cost, and resources when it first entered the market, but this is no longer true today.

Gone are the days of being held to ransom by a limited knowledge pool of resources or the shiny “best of breed” software. Today, your options are almost endless. With newer Cloud-based technologies and competition in the marketplace, there are now multiple, cost-effective options for building and running your Analytics system. More importantly, the success or failure of your Analytics project comes down to having the right consulting expertise to help understand what options are right for you and your business. There is no such thing as “one size fits all” with an Analytics system; the array of data sources and different facets or combinations of information you can ‘slice and dice’ to inform your priority decisions makes your most powerful Analytics a unique and intangible asset to your business.

An Analytics system should cater to the information needs and business requirements of the client. Teaming up with an experienced industry partner with Analytics capabilities, rather than an Analytics software vendor with general consulting services, is a great way to control the costs and quality of your project.

Myth 3: Analytics requires good data

Yes, good data is important, and everyone always wants their data to be the best quality it can be. Unfortunately, it’s often not and may never be perfect due to a variety of reasons e.g., organic system growth, mergers/acquisitions, poor processes. Bad data quality should not hold back the decision to implement Analytics. In fact, quite the opposite, it’s a solid business case for starting your Analytics journey.

One of the intrinsic, yet often understated benefits of Analytics is the ability to have full transparency into ALL YOUR DATA. It’s a line of sight into the data that is generated by all your business interactions. Think for a second how powerful that is. If implemented correctly, it will naturally bring quality issues to light that can be qualified and quantified. You can use Analytics to make an informed decision or plan to tackle data quality issues.

It may seem strange, but I would even go so far as to say that bad data quality is often the first measure of success for an Analytics project. Better yet having bad data could even ensure success. It is the first real return you will realize after going live. What’s important is that you have a plan of action to fix data quality issues once you identify them. Analytics is often the best approach for improving master data.

While your instincts are certainly correct about good quality data and successful insights being synonymous, it doesn’t mean you cannot start your Analytics project. The best approach, in this case, is to take one of the data quality concerns and make that the focus of your project. Keep the project scope small and approach it as a prototype to prove the business case.

Myth 4: Analytics takes too long to implement

This is another misconception that seems to be perpetuated by years of bad implementations.

In our experience, an Analytics implementation can be as large or small as the customer lets it – but – with the right expertise, an Analytics implementation should be exactly what the customer needs at that point in time.

There is no reason why an Analytics system cannot be prototyped to test the design and realize major benefits right away. In fact, I almost always recommend this approach over a big bang (which is trying to do everything at once). If done correctly, a customer can use the prototype to evaluate the design and then extend upon it in the future, rolling it out to additional departments and/or levels of the business.

In the case of prototyping and incremental rollouts, there is little to no throwaway work, which makes everyone happy. It’s another way to control costs and to deliver short-term benefits while also achieving long-term results. All of this does not require years of effort. Blueprint Intelligence has routinely provided such services, with customers realizing results from an Analytics system within 6 months.

Myth 5: Implementing a single vendor tool will achieve BI

I would love to be able to say this was absolutely true. However, in our experience, it is just not the case. While the software vendors will promise they can, these tools simply cannot achieve all an Analytics system should provide as functionality. Banking on this promise often leads to stretching the tool too far and data issues start to arise. While the initial release may run optimally, issues start to arise as data increases and become complex.

There is a big difference between reporting and dashboarding (visualizations). Often the two get mixed up and users become frustrated when all they have is a hammer but not everything is a nail. In our experience, an ecosystem of tools & technologies is often required to achieve successful Analytics.

At the heart of this is data. It is critical you have a strong data foundation before you even start trying to report on that data. This may mean a dedicated data warehouse or data lake. Again, this is where expertise comes in, designing the Analytics system to suit the business user’s needs, not what the software vendors promise or what the IT department believes. Like all software, Analytics must start with requirements and that means the business.

Start your project today!

While these myths are legitimate concerns, they need not be the fate for your project. Once upon a time, Analytics projects really failed to meet their expectations but in the modern age, this is no longer the case. What’s important is that you start off on the right foot with your project and not rush into starting. You need a solid plan and understanding of where you want to be with your Analtyics before your entertain anything else. It’s also best that you work with an experienced service provider who is aware of these pitfalls and can help you navigate them. Especially those of you who have enterprise-class systems such as SAP ERP or Oracle JDE.

At Blueprint Intelligence, our team has delivered numerous successful Analytics projects and knows exactly what to look for. We are enterprise experts, having delivered numerous complex implementations and solving in-depth Analytics problems for medium to large organizations. Our specialty is a practical, no-nonsense, and business-centric approach to implementing Analytics. We start with a simple roadmap and strategy with recommendations fit for your business. We never push starting a project until you are comfortable and totally on board with the approach. If you are interested in finding out more, please contact us to get started.

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