<img src="https://secure.leadforensics.com/133892.png" alt="" style="display:none;">

It is a question of survival when your industry is digitised, and you are lagging behind if you do not know how to handle or actively use your data to ensure continued growth and increased market share.

Running a business involves a large number of attempts and mistakes. The pressure of competition in the market and the digital transformation means that companies are testing everything from different marketing ideas, accounting programs or new production flows, all of which must help optimise and reduce costs.

But companies can save both time and money by collecting and actively using their data - this is known daily as business intelligence (BI).

BI makes data across the company into valuable and useful information. It allows you to identify key trends that can be used to change or implement strategic growth plans, as well as understand the link between different functions and aspects of the business, and from this find the company's next growth or business area.

"A data-driven company is an organisation where each person uses data to make better, factual decisions. Everyone has access to the data they need when they require it. Being the data drive is not about having access to a few silo reports at the beginning of each week, to conclude what went "less well" in the week before.

"It is about giving all of the company's employees the opportunity to explore data independently, and to use historical data to predict what is going to happen tomorrow. In short, it's about using its data to create intelligent and fact-based behaviour in its business,” says Karina Behr Andersen, Business Development Director, Columbus.

Becoming more data-driven

Columbus helps companies become more data-driven and attach important business-critical data from, for example, ERP, production, and other similar systems to the business strategy. This knowledge can then be passed on to employees so that they become co-responsible in their daily business and make the right decisions moving forward.

Download the guide

According to Karina Behr Andersen: "Businesses need to be challenged in their common thinking when it comes to their use of data. Most often, they do not know the full potential of the data they already have.

"Not everyone knows how they can and must be handle data, and the significance this data can have for the company if they are treated curiously and correctly.

"Ultimately, it can be an important source for new market shares, new services, new products, and savings or production optimisations.”

Focused and intelligent behaviour

BI tools allow companies to focus on and optimise their core business. But it requires special commitment and a dedicated focus to use data optimally. It is about creating a data culture where each employee, regardless of where in the organisation they are, acts on the basis of patterns that are visible in the data.

Today things are changing considerably faster than before, so we must be ready to change behaviour by making new decisions based on knowledge and be sure to drop the "how we have always done it" approach.

"Companies should not measure everything, because it only makes them defocused. KPIs must be measured directly against the business strategy to ensure the desired future.

"It's about creating a healthy transparent data culture, where each employee gets a specific behaviour based on facts. It creates a focused, intelligent behaviour and the employees can act proactively independently because they can see that it provides value for themselves and thus the business,” says Karina Behr Andersen.

Data must be vivid and visual

Most companies today claim to be fluent in data, but as with most trends, some companies tend to exaggerate. At Columbus, they have worked with BI for more than 11 years and helped some of the largest Danish and global companies, and therefore have a very clear understanding of what is required of companies that want to be data-driven.

"Companies need to visualise their data and only measure the relevance - you measure everything, you measure nothing. They need to make sure their employees get the right visualisations that enable them to make real decisions independently every day.

"In my experience, only satisfied employees take responsibility for their company's success, because they are able to do so. We all want to make a difference when we go to work,” says Karina Behr Andersen, and continues.

"We help companies to unfold their curiosity for their own data by looking at business models and making their data live so that employees can see the purpose of using data across the organisation.

"If companies do not properly use their data to "predict tomorrow", but only focus on what happened yesterday, then they do not survive in a market where the data race is occurring and where knowledge today is data-driven."

Download the guide


Discuss this post

Recommended posts

The hype around the rise of generative AI technologies makes huge promises about the potential of the technology. Yet it would be fair to say the majority of organisations are only experimenting with the technology or using it in isolated use cases.
If you organise your data and use AI strategically, you can make better decisions faster. You can for example improve your market understanding and forecasting, optimise your maintenance or reduce food waste. Choose what is most important for you!
Demand forecasters do the impossible — predict what products and services customers want in the future. Their forecasts inform decision-making about production and inventory levels, pricing, budgeting, hiring and more. "While crystal balls remain imaginary, machine learning (ML) methods can give global supply chain leaders the support they need in the real world to create more accurate forecasts." The goal is to produce exactly the amount of product to meet demand. No more. No less. Demand forecasting is used to anticipate the demand with enough time to manufacture the right stock to get as close to this reality as possible. The cost is high if you don’t get it right. Your customers will go to your competitors if you don’t have what they need. Unfortunately, capacity, demand and cost aren’t always known parameters. Variations in demand, supplies, transportation, lead times and more create uncertainties. Ultimately demand uncertainties greatly influence supply chain performance with widespread effects on production scheduling, inventory planning and transportation. On the heels of the global pandemic, supply chain disruptions and a pending economic downturn, many demand forecasters wish for a crystal ball. While crystal balls remain imaginary, machine learning (ML) methods can give global supply chain leaders the support they need in the real world to create more accurate forecasts.
If you have identified possible AI use cases for your business, the next step will be to test if they are possible to implement and if they will create great value. While there is a lot of momentum and excitement about using AI to propel your business, the reality is only 54% of AI projects are deployed. How do you ensure you’re one of the businesses that does unlock the new opportunities AI promises? Your success with AI begins by discovering AI use cases that work for your business. In the first blog of our Columbus AI blog series, we shared five areas where organisations should focus their efforts to generate ideas for AI implementations based on our experience. After generating some ideas for AI use cases that could potentially benefit your company from the first step of the Columbus AI Innovation Lab, the next step is to test which AI use cases could be operationalised by evaluating them. Columbus AI Innovation Lab
Only half of the companies starting an AI pilot project are actually executing it. The key is to choose an idea that will benefit your business. Read more about how! In 2022, 27% of chief information officers confirmed they deployed artificial intelligence (AI), according to a Gartner AI survey. Even though businesses across all industries are turning to AI and machine learning, prepare your organisation before jumping on the AI bandwagon by considering a few factors. Ask yourself: Is AI necessary for achieving the project requirements or is there another way? Does your team have the skills to support AI and machine learning? How will AI impact your current operations if you adopt it? How will you integrate AI with existing systems? What are the data, security and infrastructure requirements of AI and machine learning? The Gartner AI survey found only 54% of projects made it from the pilot phase to production. After significant investment in AI, why aren’t companies deploying it? We found the problem begins when companies define a use case. Too often, companies are not identifying AI use cases that benefit their businesses and end-users will adopt. The question is then, how should companies unlock the value and new opportunities AI promises? It starts with a systematic approach for each stage of the AI life cycle. We developed the Columbus AI Innovation Lab, a comprehensive method to address and account for all challenges when adding AI to your business operations and bring stakeholders into the process at the right time to help you operationalise AI.
right-arrow share search phone phone-filled menu filter envelope envelope-filled close checkmark caret-down arrow-up arrow-right arrow-left arrow-down