Twelve facts on creating data dynamics #DataIsTrickyNotaTrick

Data is everywhere. Organizations collect tons of data, partly without being aware they do, more often without knowing what they could do. Still, all organizations want to get value out of the data that they are aware of. So, what is next? Fortunately, recent developments in user-friendly business intelligence (BI) and analytics tools stimulate small and large businesses to engage their data. However, working without proper knowledge on data limits organizations and professionals to be successful in getting the most out of all available information. The transformation of data to intelligence remains a subtle art of interpretation, modification and representation. To give some inside tactics for mastering a data dynamic that will truly uplift your decision-making, risk management and business opportunities, we made a list of our top twelve facts on how data is approached.

Twelve facts on business intelligence and data science

Think big, start small: explore the possibilities with data, realize actionable insights with small steps by piloting, inspecting, adapting, and in pace scaling solutions. 

With cloud services, business intelligence solutions and analytics tools becoming more affordable and easier to use, the challenge to deploy a proof of concept is within a grasp. Every step is a terrific way to acquire knowledge on where to go. A journey with a growing experience while minimizing the investment. Fail fast, learn fast.

Company-size does not matter for deriving value from data. . Small-, medium- and large-sized enterprises can all improve and boost their business through data. 

Modern BI tools/platforms require less and less coding, making them more accessible for business users. Lowering the effort and reducing the size of the solutions and specialists allows all organizations to embrace intelligence, even though there is still a huge misconception about embracing BI: “Enterprises only”. Meaningful insights through data are just as important for small businesses!

Too often organizations strive for something cool in their digital transformation journey, but in their longterm outlook forget to continuously adapt to the current market scenario.   

Data, analytics and automation help organizations to make the right decisions fast without losing focus on their mission and strategy. Do not stare too much at the bigger picture, it is happening now…

Data needs to be an active part of your assets before it could ever deliver value or be monetized. 

Keeping a lot of data does not automatically make this data valuable.  To reap the value of data it must be in integral part of processes organization. The true value comes from what you do with data, from how you integrate all the information input and output into your organization.

Data governance should be applied to all data processing within an organization, not just to the reporting part. Organizations benefit from a roadmap on how they use data, so it is handled consistently organization-wide with the focus to support business outcomes. 

When you want data to support and boost business outcomes, you will consistently succeed when you ensure it is handled routinely and organization-wide. That is why data governance is crucial, it ensures business continuity.

Data can only be valuable if it is brought into context. 

Data without knowing anything about it, other than their values, is useless. Any visualization based on that data will be misleading. You need to know the (meta)data about the data to give the information enough context before it can even deliver successful insight dashboards, create business opportunities or provide forecasts.

High-end data skills help organizations to be relevant by staying ahead of customer needs.  

To be relevant organizations need to know what is unaddressed or changing in customer needs. Well analyzed data can support predictive models shifting away from reactive management. Trained and skilled data experts can turn reactive around to proactive and help organizations to be (more) successful.

Learning how to work with business intelligence tools is different from learning data science.  

To truly master the art of data science and get business changing insights for your organization, people with a combination of mathematics, programming and business skills are indispensable. Although summations and making a trendline can be useful, it is up to a skilled data scientist to find causality and reasoning for changes or behavior.

Data is generated with unprecedented pace, so collecting and cleaning data is not easy. It requires advanced data engineering skills to successfully run a data science project. Data scientists should work closely with data engineers but be in the lead of data collection, not just modeling. 

If organizations want to get maximum value out of the data, data scientist need to work in close relation to data engineers since they specify the data that need to be incorporated in their models. Compare it to for example an engineer, who is crucial for constructing a bridge to process a lot of cars; data engineers are there to smoothly process copious quantities of data, so the data scientist can create the best possible data model.

Make data security a part of the strategy from day one and use a multi-layer approach. 

Securing your data is an important topic and even critical these days. Data leakage brings financial damage for the company, and even more important reputational damage. By embracing a multi-layered approach, where security measures are taken at numerous components in your data infrastructure, intruders must pass a multitude of blockades before getting to your data.

A data architecture becomes  ever more crucial when the business grows, because subsequently your needs and landscape to successfully carry out your data strategy. 

A good data architecture reduces the complexity of your gaining data, storing data, monetizing data. Our first fact was to think big and start small. Data architecture is the think big part. It should set the horizon. The data architecture defines a standard set of products and tools an organization uses to manage data. It defines as well the processes to capture, transform, and deliver usable data to business users. Most importantly, it identifies the people who will consume that data and their unique requirements. A good data architecture flows from right to left: from data consumers to data sources—not the other way around.

Data science is not a trick. An expert can show you what fundamentals will unlock business information that can truly add value to your business.   

By acknowledging that data insights requires expertise, you can get way more knowledge and value out of your data than you think. After all, there is a science for that… And that science is empowered by artificial intelligence and machine learning to truly boost results.

To wrap up

Of course, there is a whole lot more to add to this list. But the first priorities for organizations to take care of when they want to put a successful data strategy in place, is ensuring data governance, data architecture, data quality, data security and high-end data skills are priority number one. You can never make an accurate data visualization or create a trustworthy insights dashboard if your data is just a collection of a lot of needles in a haystack. In that case, it lacks the structure and context that only true data science can provide you with.