A life beyond the dashboard

dashboard analytics

Simon Rolph, founder of data analytics firm, Such Sweet Thunder, tells us how businesses need to move beyond the dashboard, embracing a new era of dynamic and directed analytics.

Data is integral to everything we do in business – right from reporting to decision making. The role that dashboards play in our data lives has changed significantly, making our data tracking analysis time-efficient, synchronized, automated – even simple perhaps.

However, what if we said this was ineffective and could be the cause of being left behind? As business needs develop, data analysis needs changes too, and dashboards are pushing businesses into grey areas where they can’t analyze data in the ways they need to.  Whilst dashboards are fit for the purpose they were created for, businesses are now looking for a solution to data analytics that can be fluid and fast-changing. Analysts could be looking to measure one part of the business one day, and then a different part the next, and dashboards can’t provide the fluidity to keep up with this demand.

What Is A Dashboard In Data?

Mentioning a dashboard to those who aren’t ‘in the know’, or only have a passable knowledge of data analytics, the first consideration is that of a car. Whilst there are clear and obvious differences, the concept is still the same – a series of indicators that take raw data from the car, transform it into workable information, and then relay said information to the driver. These indicators could include speed, car analysis, or faults.

Whilst there are marked differences between the dashboard on a car and that of data, the fundamentals remain the same. A data dashboard is a tool businesses use to help track, analyze, and display data, usually to gain deeper insight into the overall wellbeing of the organization, a department, or even a specific process. Dashboards have been used in business for many years, and for the vast majority of this time, they’ve been highly credible and vital for operations.

Why Do We Need To Move On?

Times are changing, and as such, dashboards are becoming less and less vital. In their place, a modern alternative must step up and fill the void. Ultimately, dashboards are becoming outdated, and future analysis methods need significant modernization to not only be fit for purpose but continue to be the best option in the years and decades to come. There are some prime reasons why dashboards are becoming outdated and why the upgrade needs to happen to more relevant and fit-for-purpose alternatives.

Ultimately dashboards are failing for three main reasons:

  • They’re naturally inflexible in nature
  • They’re built without a user in mind
  • They can’t interpret data into usable information.

They’re Naturally Inflexible in Nature

Dashboards are notoriously rigid and are ultimately a blunt instrument. They present all data in the same way, all the time, and it’s always raw data. The key to this issue though is that they can only really search for what they’re being asked to – there’s no mindset beyond the dashboard and no interpretive qualities. They’ll pick out information on what they’ve been programmed to do, and that’s it. Businesses nowadays are having to be even more flexible and fluid than ever before, which isn’t a style that suits a dashboard. The modern CTO or IT infrastructure will need one data set one day, and another the next, which a dashboard simply can’t provide.

They’re Built Without A User In Mind

Dashboards aren’t at all personal – if anything they’re one and another with each one. There’s no specification to the person, or the business they’re collecting data for. They do one job across the board and don’t have the capabilities to drive any deeper. Dashboards are often based on a set of assumptions and priorities, which are often set without a deep enough understanding of the business, so the dashboard will rarely be measuring everything that a business needs to know unless significant care is taken in the setting up process.

On that subject, dashboards are notoriously complex to set up, often requiring help from an IT expert, developer, or a reporting tool that features pre-loaded dashboards that don’t gauge metrics that are pertinent for all users. Even once they’re set up, it can be challenging from the onset because specific software is meant for experienced developers, making them complex to set up by anyone outside the IT field.

An additional drawback of dashboards falls into the realm of human interaction, and the fact that we as users become too comfortable, and begin to ‘go through the motions’ rather than apply some genuinely critical thinking. Because dashboard users see the same data every day, they can become ‘programmed’ in what they’re looking at, which then leads to a lack of consideration and analytical thinking. Users have become too comfortable with dashboards, and aren’t applying the right-thinking more often than not, which leads to the human mind not ‘seeing’ anything anymore from a data perspective.

They Can’t Interpret Data

The final biggest failure of a dashboard is that it can’t interpret data. It can pump out raw data into the hands of a human, who then ultimately has to undertake the task of interpreting that data into really workable, usable, decision-making information. This then factors in the flaws of human error, mistaken interpretation, or data being misread and then miscommunication.

What’s The Solution?

The solution to dashboards – and moving on – lies within directed and dynamic analytics. Dynamic analytics essentially do what they say on the tin – the data analysis they offer is flexible, fluid, and constantly evolving. For obvious reasons, this goes above and beyond the capabilities of a dashboard, collecting, collating, and then analyzing a multitude of data in a short time period. The idea of storytelling will also be key to a ‘post-dashboard’ world – putting the analysis into context, and then retaining said context for future reference, looking for those all-important trends and patterns.

However, there is a challenge when it comes to dynamic analytics – and it’s a big one. In order for this modern, fluid data collection process to be a true success, the quality of your data has to be impeccable. When the collection of data isn’t of high quality and is laden with errors, it will ultimately cause the information or analysis to be unreliable, and could easily lead to errors being made. When there is such a reliance on this data, and such a reliance on its ability to aid us in making decisions, any flaws or weaknesses in the data itself could lead to catastrophic mistakes, and turn your informed business decisions into no more than a stab in the dark.

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Ultimately, dashboards are becoming outdated, out-thought, and aren’t fit for the purpose they were originally designed for. It’s vital that business leaders look to adopt dynamic analytic tools to keep up with the curve and get the best usable data to make their decisions. However, CTOs and decision-makers do need to be aware of the risks of using dynamic analytics, and how important it is to have data of the highest quality. If you take all of these considerations into account and act on them accordingly, then you can enjoy a happy and healthy data set, ready to make the very best business decisions.

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Simon Rolph

Simon Rolph is the founder of data analytics firm Such Sweet Thunder, and has been CEO since its inception in 2007. As an experienced interim software engineer, business analyst and IT project manager, specializing in Data Management and Analysis projects, Simon has over 25 years of successfully delivering complex, high-value cross-sector projects and programs for ‘Blue Chip’ internationally renowned organizations.

Simon’s goal as CEO of Sweet Thunder is an aim to create a great environment for people to work delivering simple solutions to complex problems that make a tangible difference for our clients.

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