Break down data silos and put data into the hands of the many

Gone are the days whereby it is acceptable for data silos to exist. A siloed approach to data analytics is beyond outdated.

In order to make a company more data-driven and analytical in its thinking, we first need to break down the existing barriers of data silos. However, this is no easy task. Not only are we suffering from a skills gap — but, we also face the battle of today’s data literacy divide, with reports from Gartner highlighting that by 2020, 50% of organisations will lack sufficient AI and data literacy skills to achieve business value.

Data is one of the most powerful tools we have in the business world. Capable of unlocking the greatest business potentials, we ought to be analysing our data to gain the deepest insights possible. However, at the moment, we’re seeing data insights in the hands of the few — not the many.

Top five trends for harnessing data in 2019

From data storytelling to using data for good, James Eiloart from Tableau gives his take on the top trends in harnessing data as we head into 2019. Read here

Data: from the few to the many

Whilst data in the hands of a few experts can be powerful, we must question whether this is enough to facilitate positive change. Think of it like this. Business users work with data each day, but are currently confined to basic reporting, reliant on making requests to the data scientists in the organisation when analysis and interpretation is required. This is because they are not equipped with the skills, knowledge or tools required to analyse the meaning of the data.

We need to move away from the existing elitist idea that companies are solely reliant on data scientists to really excel, because this simply isn’t sustainable for the workplace today. In fact, Gartner expects that by 2020, 80% of organisations will initiate deliberate competency development in the field of data literacy to overcome extreme deficiencies. The real value of data can be found when data is made available in a usable format – and crucially, is made accessible to employees across every business division and function.

You may ask why this is the case. Well, data silos cause businesses to be inefficient. Ultimately, in any business, it is not possible to make a data-driven decision without high-quality data to inform it. Within every business, management teams need to be focused on breaking down the pre-existing data silos, ensuring that information is accessible to employees across the company. The more accessible data insights are to business users, the more enterprise-wide value they can get out of them.

Tear down silos the data lake is the future of business data

Druva’s chief technologist, W. Curtis Preston asks: where is your crucial data stored and how easily can you access it? Read here

Breaking down data silos

Ultimately, any organisation can only start to break down data silos by deploying a holistic data strategy. Good data analytics comes from a solid data management foundation. This means understanding that data exists and in what systems. By properly managing, cleaning and combining data, we can then get the right data to the right people, at the right time.

Currently, one of the biggest barriers to this is a lack of data strategy. This often leads to a disparate approach, meaning that basic business questions cannot be answered without significant data preparation work or without specialist data scientists being deployed. Good data management will break down these silos and offer a more holistic view of the data, making it accessible to all. This is often referred to as the democratisation of data.

To achieve real data democratisation, we need to first understand the areas we need to implement change.

1. Skills — Companies need to think about whether or not they have employees with solid data management skills before they start thinking about employing even more data scientists. It’s about building a solid foundation before the tower blocks go up.
2. Technology — The right tool for the right user is essential. Most questions do not require a complex deep learning model to answer them. They simply require tools that can surface well-structured data. Whilst there are always going to be some questions that require the input of a highly skilled and trained data scientists, this isn’t necessary every time.
3. Data — Disparate systems, transactional messy data, data warehouses and lakes that don’t quite meet expectations are becoming the norm. It’s time to go back to basics. We need to think about the questions we need answers to and start from there. This means caring for data – especially if we want to reap the results.
4. Company-wide processes — Understanding your process is the first step. However, to take it to the next level, we need to map the data that underpins those processes. Where is it coming from and what format is it in? Will this work for the desired outcome?
5. Cultural changes — Today, data is no longer a specialism. It’s a fundamental skill expected of anyone in the business world.

Recruiting and empowering data scientists a must for success

Data scientists’ presence in organisations is growing. It needs to. But, because demand outweighs supply, organisations must get creative in how they recruit and empower data scientists. Read here

We can’t all be data scientists. But, we can all understand the fundamentals. Business leaders should take the opportunity to work with IT to break down data silos and put data into the hands of the many. At the end of the day, two heads are better than one. It’s time we remember that data is the most valuable asset in any organisation – and in democratising the data, we free up businesses to do more with the insights.

Written by Joanne Taylor, director of Digital Strategy, Software AG
Written by Joanne Taylor, director of Digital Strategy, Software AG

Editor's Choice

Editor's Choice consists of the best articles written by third parties and selected by our editors. You can contact us at timothy.adler at stubbenedge.com