7 Things data driven decision making means in practice

· Thomas Wood
7 Things data driven decision making means in practice

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Data-driven decision making (DDDM) is all about taking action when it truly counts. It’s about taking your business data apart, identifying key drivers, trends and patterns, and then taking the recommended actions. The end goal is to get this information into the hands of people who can make informed decisions.

However, many organisations struggle today to reap the true value and benefit from their data-driven decision making.

Artificial Intelligence for decision making – Driving better value through decisions

The challenge that most businesses face today is understanding how to make strategic use of massive volumes of data – not only to improve decision making but also to drive better ROI. Artificial intelligence for decision making is something businesses should understand, as it can help them interpret the data they collect in a way which shows patterns, trends, and insights to inform their strategy-building and decision-making.

If you’re coming across the term “data driven decision making” for the very first time, then it is the process of utilising data and analytics to drive business decisions. But why is this important for every business across nearly every sector to understand?

We live in a fast-paced and largely data-driven world. Businesses are fighting tooth and nail to stay ahead of the competitive curve, which has made DDDM an essential tool for making informed decisions to drive growth, success, profitability, and efficiency. The days of relying on the advice of “experts” or only your own gut instinct are long gone. Without data-driven decision making, you simply cannot expect to make big decisions which lead to improvements or positive changes within your organisation.

This is why we’ve prepared this in-depth article to help you understand the benefits of data driven decision making, seven common ways to explain what it means in practice, and real-world industry examples to show you how the big players are doing it right.

What is data driven decision making?

Intuition or gut feeling may prove to be beneficial in certain situations, but when it comes to business decisions, it would be flat out foolish and ignorant to base decisions on just instinct. In fact, most of your decisions ought to be based on concrete data and the insights you extract from it. After all, you can’t quite record, verify, or quantify gut instincts or feelings, can you? This is why data driven decision making and artificial intelligence for decision making exist.

If we attempt to understand what DDDM or data driven decision making is, then it is basically the process of making decisions based on actual, quantifiable and verifiable data, as opposed to just guesswork or gut instinct alone. While it’s important not to ignore your business acumen and gut feeling for making day-to-day decisions, you’ll still need to make the vast majority of those decisions through data analytics and business intelligence.

With the emergence of technologies around data analytics, business intelligence is now more accessible to non-data analysts or specialists. The trend – referred to as data democratisation – has laid out even more opportunities in front of businesses, when it comes to collecting, storing, and processing data from their daily business activities. This data that you collect, for example, on your customer service and sales, or your procurement and inventory, can be used to make key business decisions, and you don’t necessarily need to install any expensive IT infrastructure or support networks to reap the benefits.

DDDM activities typically involve:

  • The collection of data through metrics and specific KPIs (key performance indicators)

  • Identifying and analysing the patterns, trends, and insights within that data

  • Using those insights to craft business strategies and activities to boost operations, improve efficiencies, and achieve goals, among other things

Data driven decision making is not something that just sounds “important” and looks good on paper – it generates real results. PwC conducted a survey involving over 1,000 senior company executives where it was reported that data-driven organisations are 3x more likely to make better decisions than those which do not engage in any data driven decision making.

What does data driven decision making mean in practice?

Even though businesses invest heavily into technology and services pertaining to data analytics, they often find themselves at crossroads between the amount they are spending to acquire those capabilities vs. how effectively they are actually using them to their advantage.

In one survey where 64 C-level executives participated, 72% said that they were able to nurture a culture of data driven decision making in their organisations. Clearly, businesses need to do more to reap the true value and benefits of ‘artificial intelligence for decision making’.

The disconnect is often a result of not properly understanding what it entails to be a data-focused organisation – because it isn’t just about the technology and quality of data, but it’s more about nurturing the right culture and internal processes to make data driven decision making the standard.

Here are seven things involved in data driven decision making which may help you understand what it is in practice:

1. Identify personal biases

It’s only normal that personal biases exist in nearly every business, although it does make it rather difficult to make data-driven decisions in a completely objective way. People will almost always see what they want to see or expect to see. Luckily, you can prevent this bias from contaminating your DDDM strategy:

Businesses must first acknowledge bias as a ‘challenge’. By being aware that a bias exists, you can start working toward reducing its impact.

Plus, it’s always a good idea to work in teams, which will ensure that your data analysts have each other’s backs.

Look for any conflicting data and then ask questions to test out assumptions and initial findings.

2. Speed up the data collection process

The day you decide to be a data-driven company is the day you begin your data collection. However, you need to make a proactive effort to not only collect and log that data, but also create a proper system for cleaning it up and organising it as you collect it – because there will be massive volumes to work with!

3. Ask questions (the right ones, that is)

When you ask the right data analysis questions, it help your team to focus on relevant data only. It can be too easy to go down the data collecting rabbit hole where everyone is chasing leads that end up nowhere.

This actually goes back to the objectives you task your data analysis team with. For example, what do you want your team to learn from the data or what kind of insights should they extract? What kind of KPIs will be used to measure the different variables? What will be the source of the data?

4. Hunt for the right data to answer the above questions

Now it’s time to find the data to answer the above questions. So, you need to know whether this data has, in fact, been collected previously, or if you’ll need to have new internal and external mechanisms put in place to acquire it.

5. Revisit and reanalyse the data

Your data analysis team should be able to take a step back at this stage and rethink how they interpret the data. Changes can often occur, but analysts shouldn’t see them as failures but rather opportunities to learn as well as improve their analytical practices.

Understanding where they went wrong and then rectifying the missteps right away will produce far more accurate results.

6. Present the data in a meaningful and interpretable way

The findings from your analysed data can only be used to your advantage if they are presented in a meaningful way. For example, by integrating the right software tools, your data analysis teams may be able to create a bespoke dashboard which tells a fully updated data story, helping everyone from managers and CEOs to key stakeholders make effective decisions based on that data.

For instance, a dashboard for sales forecasting should contain an ‘at a glance’ overview of all the relevant KPIs like net profit margin, estimated earnings, operational expenses, sales for a specific period, and so on.

7. Set decision-making goals that are measurable

This is where you’ll be making business decisions. However, your data driven decision making is only as good as your organisation’s general business goals and strategies – if the two are not aligned, then you cannot set measurable goals around decision-making.

Therefore, it’s important for your data analysts to set measurable goals as this ensures that everyone is on the same page.

What are the benefits of data driven decision making?

Before we discuss the benefits, it’s worth discussing an organisation’s ability to work with data that’s accurate. After all, to reap the full benefits of data driven decision making, the data you work with has to be accurate.

Data management practices, including DI or data integration, will help you navigate this challenge by consolidating all your quantifiable information from multiple sources, into a single dataset which shows a real-time view of your business performance. The integration of machine learning and artificial intelligence for decision making with DI systems will further enhance their ability to store data that’s not only accurate but also contains full data values and metadata.

Finally, to reap all the benefits of data driven decision making, organisations must get into the habit of persistently re-examining, questioning, and reanalysing all of its data-driven decisions. Furthermore, organisations may need to change up their business goals a little – depending on their unique circumstances and individual business landscape, of course – and this often means taking a completely different data analysis approach.

As for the specific benefits of using the latest technologies such as machine learning and artificial intelligence for decision making:

More cost-savings and better resource allocation

Data driven decision making (DDDM) can help your business allocate resources far more efficiently, thus, leading to cost savings. When you analyse your operational data, you can immediately identify areas with inefficiencies, allowing you to make adjustments in order to optimise performance. In turn, you’ll be able to optimise resources as best as possible and reduce waste which will, again, lead to cost savings.

In one of its surveys, the Harvard Business Review reported that 49% of businesses that took up data projects to cut down operating costs saw highly positive results.

More accurate and efficient decision-making

Perhaps, one of the biggest benefits of DDDM is increased efficiency and accuracy in your day-to-day decision making. Data can provide you with insights into everything from market trends and customer behaviour to operational performance and competitor weaknesses, all of which can help identify key opportunities to make more informed decisions. And, by integrating the right data and data analytics tools, you can make strategic decisions in real-time, which will undeniably enhance and improve the accuracy as well as efficiency of those decisions.

With a reasonable amount of time and practice, you will learn to leverage your analysis through data-driven decisions in a highly proactive way; for example, by determining how well-suited a product is for a specific market or by forecasting demand for a specific product or service.

Additionally, using data to guide your day-to-day decision-making processes will lead to more objective decisions being made which can be verified, tested, and also replicated under specific conditions.

Deeper understanding of customer needs & preferences

Data driven decision making helps you gain a better understand the needs and preferences of your target base. As you analyse your customer data, you’ll be able to pinpoint trends and patterns which can be used to inform both your marketing and overall product development strategies. This can help you address customer needs and preferences more directly, which will lead to better customer loyalty and satisfaction, for one thing.

Manage and mitigate risk more effectively

Businesses using machine learning and/or artificial intelligence for decision making are in a much better position to mitigate and manage risk as it arises. By analysing specific data, you can zero-in on potential risks, identify the source of those risks, and quickly take the necessary measures to eliminate and manage them in future.

This has become very important in today’s business landscape as it can help you steer clear of costly mistakes (which often cannot be undone) and minimise the impact of any risks and threats to your business, as well as adverse conditions rising due to unforeseen events or circumstances.

Step-by-step guide to implement DDDM the right way

While data-driven decision making does help businesses to make more informed decisions with their data, there are specific steps that must be followed to successfully implement it and achieve optimal outcomes (some of which we already discussed under a previous section in the article):

Step #1 – Define objectives and KPIs

Right off the bat, you must define your business objectives along with the KPIs to target, in order to measure how you’re progressing towards the said objectives. Therefore, it’s important to take a moment to understand what it is you want to achieve and how you will measure that achievement.

Step #2 – Identify the appropriate data sources and start collecting data

Once you are certain about the business objectives and its respective KPIs, identify the relevant data sources, as these will provide you with the data needed to measure progress towards the various KPIs. This may include anything from your financial, customer data, and sales data (internal source) to your social media and market or sector data (external source).

Step #3 – Analyse and interpret the data using the right tools & techniques

After collecting the relevant data, you must analyse and interpret it, and this can only be done using only the right tools as well as techniques. This may include machine learning algorithms, data visualisation tools, and statistical analysis, all of which will help to unveil specific patterns and insights in your data.

Step #4 – Communicate the insights & findings to your team and/or stakeholders

After analysing the data and identifying the desired insights and findings, you need to communicate them to the key decision makers in your company, and that too in a very clear, digestible, and concise manner. If you are the sole decision maker, it would still be good practice to do the above, so that your team can refer to it when they must.

This may be in the form of dashboards, reports, or presentations which are easy to understand and highlight all the important findings, as well as the actionable recommendations or ‘next steps’.

Step #5 – Monitor & evaluate outcomes

The final step to make your data driven decision making as effective as it can be is to monitor and evaluate the outcome of the above – along with adjusting your strategies as and when needed. This may involve tracking the progress of your key objectives and KPIs, evaluating how effective your data-focused decisions are, and making the necessary adjustments to ensure perpetual success.

Real-world examples of DDDM done right

Ford – Predictive maintenance and quality control

Ford regularly uses data analytics to improve both the quality and reliability of the cars and trucks it manufactures. For example, data is collected on the various vehicles' performance, including readings from sensors and engine diagnostics, in order to predict the next scheduled maintenance.

The data-driven approach allows the American auto giant to proactively schedule maintenance calls, therefore, reducing the wait time and downtime for its customers, as well as ensuring optimal reliability of its vehicles.

Ford also uses data analytics to identify any potential quality issues fairly early on in the production phase, so that they can make tweaks to prevent defects before they actually occur. By leveraging data this way to improve quality control and maintenance processes, Ford has been able to provide a superior customer experience and even reduce the number of warranty claims and the costs associated with them.

Netflix – Content curation and recommendations

There’s no denying how Netflix has changed the way entertainment is consumed across the world, and it has been one of the earliest adopters of a data-driven approach to curate content more effectively and make sound production-related decisions.

Netflix collects data on the content its subscribers watch, where they watch it, how they watch it, etc. and then uses it to come up with more personalised recommendations – as well as to inform its decisions around content acquisition and production.

For example, the iconic Kevin Spacey political drama, House of Cards, was aired by Netflix based on insights which indicated that subscribers who had previously enjoyed the British version were also big fans of Kevin Spacey and David Fincher, the director. The data-driven decision resulted in a critically acclaimed series which was watched all over the world and received beaming reviews.

Walmart – Better optimised supply chain

Walmart is very well-known for the way it manages its supply chain, and data analytics have a lot to do with that successful management.

Walmart collects a variety of data on its sales trends, inventory levels, and customer demand in order to optimise its day-to-day supply chain. Through data insights, they ensure that the desired products are always available on store shelves, so that there’s never an inventory shortage or excess.

The company regularly uses predictive analysis to find out which products will likely sell out during, say, a natural disaster or calamity, so that they can supply those products quickly to the affected areas. This data-driven approach has allowed them to quickly replenish supplies in disaster-hit areas, and its efforts are appreciated tremendously by Americans and even those living outside the US.

Conclusion

Leveraging data driven decision making is one thing, but doing it right is another. The benefits are also clear as daylight – from more accuracy and efficiency in decision making to cost-savings and better serving customers.

If you want to fully take advantage of what data driven decision making and technologies like artificial intelligence for decision making have to offer, schedule a consultation now: +44 20 3488 5740.

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data driven decision making

artificial intelligence for decision making

Data driven decision making is done through data analytics, where businesses can uncover crucial insights and trends to make better informed decisions.

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