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

Are we throwing common sense and good business insights out with the data-driven bath water?

Karina Behr Andersen
Jan 24, 2020
Karina Behr Andersen
Home
  • EN-GB
  • Are we throwing common sense and good ...

Being a data-driven business is now to be expected. And rightly so. Here is a short truth… Either you make sure that your business is driven by data, or you will soon be overtaken by your competitors that are maximising the power of data...

Businesses and their teams today can only achieve growth by becoming data-driven.

So, what does it take to be data-driven? In my opinion, it is not rocket science, but a fundamental discipline that we – consciously or unconsciously – go on the journey towards being more data-driven, by implementing the fundamentals of good data management practices.

Business Intelligence (BI) has been stamped as old-fashioned, inflexible, costly, cumbersome and quite unnecessary as we have now got new exciting and agile technologies. With the advent of machine learning, AI, Big Data, advanced analytics, IoT, which can accelerate the company’s maturity on the data-driven curve.

How will your business be data-driven?

Here, I must be the one who destroys the good mood by telling a slightly longer version of the truth about what it takes for a business to be data-driven.

Machine learning, Artificial Intelligence (AI), Big Data, advanced analytics, Internet of Things (IoT) and similar technologies are fundamentally based on data - something as simple as zeros and ones. We can collect, leverage (existing!) and compile data in a sensible and meaningful way, which can be acted upon, which determines whether a business is data-driven or not.
Microsoft Power BI training from Columbus UK

The journey towards becoming data-driven is like building a rocket, but it starts with solid business intelligence craftsmanship and the ability to leverage and act on data – no matter how complex you plan your journey to be.

At the end of the day, anyone who wants to can build a rocket – you can even do it yourself. Perhaps you only need your ‘rocket’ to be fired once, but then it will become expensive and time consuming to take off on your data-driven journey the next time you are required.

The journey towards being data-driven

Therefore, you need to find a partner who is a professional rocket builder, a partner that has the expertise to pull knowledge and insights out of data, which you can act on helping you to create further business value.

The right partner is one that has a good method of building a solid rocket, that can be safely fired and which you can continue to build as your business develops and your needs change. Unsurprisingly, it doesn't make sense to be driven by data on the wrong basis and in the wrong direction, so to speak. Your partner will guide you here.

They will have the expertise, experience and proven ways to take you safely on the journey towards being data-driven and will consider key factors that help to determine whether you're achieving the expected return on investment.

You should also…

  • Find the right technical solution and architecture, that you can easily build on 
  • Choose the right method for conceptual modulation and visualization
  • Look for a solution with an intuitive and easy-to-understand dashboard, which ensures that you can analyse and act on the data it presents 
  • Take care of user adoption, it’s very important – make sure that it is useable in everyday life for your team
  • Select and put together your BI team and processes wisely – this must steer your journey in the right direction
  • Good data in, good data out – you should set up processes that ensure that your solution always runs with accurate data
  • Introduce an efficient and easy support setup for the solution, whether internal or with external assistance
So – to summarise, whether you are successful on the journey towards becoming data-driven, depends 100% on the ability to collect, utilise and act on your data.

The discipline has been the driver for business intelligence for years and it will continue to be the driver for all new technologies – whether they are called machine learning, AI, Big Data, advanced analytics, IoT, or something completely new.

In return – if you get the right rocket built from the start – you will have a strong base in place to take advantage of these new technologies, without having to start all over again, or make expensive and time-consuming backflow and stud shooters!

Microsoft Power BI training from Columbus UK
Topics
Karina Behr Andersen

Karina Behr Andersen

Karina Behr Andersen er Business Development Director i Columbus og beskæftiger sig med forretningsudvikling, Analytics samt Business Intelligence. Karina har over 20 års erfaring fra branchen samt med ledelse fra både branchen og fra produktions-, salg- og servicevirksomheder. Hendes særlige passion er, at sikre kunderne vækst og intelligent adfærd gennem det rette fokus i de daglige forretningsmæssige beslutninger og deres datadreven forretningsudvikling. Hun skriver primært blogs om brugen af data for at understøtte strategien, øge markedsdele og nye kerneforretningsområder.

Find me on:

Discuss this post

Recommended posts

How AI can solve challenges for food and beverage manufacturers

Columbus

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!

Machine learning: Bridging gaps in demand forecasting

Santosh Kataram

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.

How to select AI use cases to help your business succeed

Avinash Gundra

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

Applying AI: How to define valuable AI use cases

Santosh Kataram

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.

Why your AI project won't succeed unless you transform your data

Naga Chaitanya

It doesn’t matter how good your artificial intelligence technology is. If you have bad data, you’ll get bad results. While AI is one of the most innovative and impactful innovations for businesses today, bad data can prevent AI projects from getting off the ground. Having high-quality data and following data preparation best practices are crucial for a successful AI project. In the Transform phase of the Columbus AI Innovation Lab, the one we detail here, data is collected and prepared for use by AI and machine learning (ML) algorithms. Major phases in Machine Learning Machine learning, frequently used synonymously with “artificial intelligence,” is about using predictive methods to simplify tasks. There are several essential steps to building ML models, and these steps can change based on the issue you are trying to solve and the kind of ML model you are building. However, in general, the steps involved in creating and implementing an ML model are as follows: Define: Different approaches for finding AI use cases Discover: Scope and prioritise ideas before developing an AI solution and implementation strategy Transform: Transforming the business case including data acquisition and preparation with AI/ML (current blog) Model and evaluate: Analyse and enhance the business use cases using AI/ML algorithms Deploy and support: Deployment of AI/ML models and support/monitoring to guarantee quality and effectiveness

Filter posts

Archive

Subscribe

Subscribe to the blog
right-arrow share search phone phone-filled menu filter envelope envelope-filled close checkmark caret-down arrow-up arrow-right arrow-left arrow-down