5 Critical Fail Points In Keeping Your Ship Afloat In A Sea Of Data
Throughout history, the people with the better access to information and the ability to do something effective with it tended to succeed where others did not.
The ancient Spartans were defeated by the passing of details of a small mountain trail to the Persians.
Abraham Lincoln coordinated the successful Union Civil War effort via the use of an extensive telegraph network that the Confederates did not have.
From warning fires on mountain tops to smoke signals, war drums, signal flags, flares and message runners, humanity has always valued the collection and dissemination of pertinent information in trying to navigate the times.
Today, we are floating (or sinking) in an exponentially expanding sea of data, and it seems like everyone is talking about Business Intelligence (BI) as the magic bullet to taming it.
However, there are 5 critical fail points that could sink your organization if you get them wrong.
What is BI?
First of all, let’s define the field of discussion. Business Intelligence is a technology driven process for analyzing business data that provides actionable insights to executives, managers and other corporate users to make informed decisions.
A usual BI routine includes data visualization and reporting in the form of dashboards, scorecards, KPIs, metrics, ad hoc querying, automated monitoring and alerts, etc.
But, 5 fail points must be navigated effectively to enable, manage and protect business growth in today’s highly competitive data driven economy…
Fail Point 1: Visualization is the most important part of successful BI.
It seems that everyone loves a great picture. And by great, we mean the latest, shiniest, most visually responsive, funky representation we can find. Unfortunately, this is like buying a stereo system (back in the day) on the basis of which one had the most flashing lights. The flashing lights became a distraction – and a proxy for what really mattered – the sound.
Today, snappy visualisations have become a distraction and proxy for what really matters: getting data that’s worth observing.
Organisations gets so focused on how the ship looks above the water line that they forget that the running gear – the engine room, propellers, rudders, and hull integrity determine how fast, and in what direction, the ship moves.
The challenge is that like ocean liners, businesses of any real size take time to change direction. Poorly managed, discrepant data can give conflicting results over a short time frame which means that your ship could be steaming ahead in the wrong direction, or losing much needed momentum constantly trying to change direction, or worse, drifting aimlessly towards the rocks while different factions argue about the direction.
Fail Point 2: Data is data…right?
Data is the root cause of BI and analytics. In this generation of Big Data, obtaining relevant and trustworthy data as well as formatting data before visualization is more important than ever.
Lack of effective data management and security is one of the highest areas for erroneous levels of reporting. Inefficient data cleaning and lack of data analysis can also quickly generate inaccurate reporting and poorly formulated decision processes.
It is important to remember that if you wait until the data is 100% correct before moving, your ship will never leave dock.
So, it is absolutely critical that you get a set of tools that allows you to manage this and highlights discrepancies quickly so that changes can be made once you are under way.
Fail Point 3: Only Management Needs Access To BI
Context is critical to effective decision-making. It provides the background for the data – and shades the results as either good, bad or indifferent. For example, if I told you that your ship was going 10 knots at a heading of 160, it actually means nothing. It is only when we provide the context that the ship needs to get to its destination 50 nautical miles away in 3 hours at heading 165, that we realise that the ship’s performance is not going to get us to where we want to be, when we want to be there.
Often the people with the most context are the people at the coal face. But equally often, BI access is restricted to management – and often just to higher level managers – 2-10 layers away from the coal face. Now, while it is true that the managers may have extra context width that the line level team do not, it is also true that the line level team has context that cannot be captured in numbers.
For example, line level sales team members deal with the sentiment of customers on a daily basis. They may well see that customers are buying, but that they are not happy about it, and would happily buy from someone else, or would delay buying at all if they could. Your line level sales team could tell you that business is great now, but it is just a matter of time before it is not. By putting the appropriate BI tools into the hands of the line level teams, you can empower them to make appropriate decisions for the long term success of the business.
Fail Point 4: All governance frameworks are essentially the same…
One of the reasons that BI is not disseminated down through an organisation is that the governance structures around it become very complicated and expensive to maintain.
This is directly related to the tools that you use, because some tools do things very differently for vastly different results.
Choosing a tool that lacks a robust, built-in governance framework will require a separate governance framework to be constructed for each BI application created on your platform.
With the typical number of applications rolled out running into the hundreds, this will be very expensive to maintain in the long term – and therefore less able to pivot as required. The net effect is that often it will never see the light of day with the people that could really use it.
Fail Point 5: Simple analysis is all we need
An error free BI architecture with data that is worth observing, managed by a serious governance platform is a great starting place, but that is not all. In the sea of Big Data, advanced analytics is equally important along with BI to implement a successful growth model.
Operational utilisation of advanced analytical techniques like statistical analysis, data mining, predictive analytics, and machine learning, is becoming the norm to gaining deeper and more meaningful insights for your business.
While regular BI is essentially reactive, advanced analytics is proactive, and can even be prescriptive.
Regular BI helps to answer business questions, like what happened, when it happened, who is responsible for that and how many such events occurred.
Advanced analytics helps to answer more complex questions like why did it happen, will it happen again in future, what will happen if we change and gain more insights from data that we never thought to ask.
Sure, Business Intelligence helps to remove guesswork and boost productivity, improve visibility and turn data into actionable insights about consumer behavior, sales, market trends, goals and achievements, opportunities and much more.
However, advanced analytics helps to gain a better understanding of past, present and future of your business by prediction and recommendation techniques. It is like looking beyond the horizon to chart a better way forward rather than waiting to respond to whatever appears.
Conclusion
The sea of data in which our businesses sail is growing exponentially. The interconnectedness of our world is seeing the impacts of small decisions on one side of the globe rippling across the horizon towards us – whether we like it or not.
The tools exist to allow us to make sense of this data tsunami, but the time to use them is now. Take the opportunity to navigate the fail points, and you will avoid being set adrift in a sea of failure.
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