Data Analytics in B2B Decision-Making


Businesses rely on data analytics more than ever today to guide their business decisions. Chances are, whether you run a Fortune 500 company or a small local firm, you use data analytics tools to assist with key business choices.

Despite the fact that some firms may be unfamiliar with the idea of data analytics, the practice of using analytical reasoning to improve business procedures has been introduced previously.

Businesses can identify trends and discover methods to enhance crucial operations that have an influence on their bottom line by analysing vast amounts of data. To name a few of these processes, there are sales, marketing, and delivery.

Let’s examine more closely how organisations can utilise this essential technology as data analytics is so crucial to today’s business world.

Businesses from all sectors communicate with clients and look for new business prospects through the business-to-business (B2B) decision-making process.

Knowing how to use this data to improve company decisions successfully is crucial at a time when customer data is more valuable than ever.

Spreadsheets, emails, and continuous conference calls are frequently used in B2B firms to make decisions.

It is simply no longer sufficient in the modern world to analyse and derive conclusions from corporate data using old tools and processes.

Companies are relying on data analytics technologies to glean useful insights from the mounds of information they acquire, saving time from poring over tiresome spreadsheets and unreliable data analysis.

The data analytics tool will serve as a catalyst for better decision-making by revealing patterns and resemblances in the data that would otherwise be challenging to find without the aid of sophisticated tools.

The key to making the most of your data analytics project is to set up your tools correctly and choose the appropriate data for your research. By equipping yourself with the appropriate knowledge and resources, we’ll cover how to use this method in this post.

Why Do Companies Need Data Analytics?

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Businesses need to understand client behavior in order to succeed in today’s digital world.

If you don’t know how to evaluate and measure the value you offer, whether you’re selling products or offering services, you’ll quickly discover that your clients will go.

You need to be aware of if your marketing efforts are having the desired effects and if the target market is being reached. Data analytics alone can achieve this.

The problem is that not all firms are the same, and some are considerably more ready to benefit from data analytics than others.

For instance, larger businesses with a loyal customer base and high levels of employee engagement frequently gain the most from data analytics.

Startups, on the other hand, may find it challenging to obtain the data necessary to make informed decisions if they have a little marketing budget.

Don’t worry, though; there are many ways that businesses without a lot of marketing skills can leverage data analytics to their advantage.

Make Your Best Efforts In Businesses Be Known


If you’re reading this, I’m going to presume that you’ve already decided to use data analytics in your company. Congrats!

You’re one step closer to being able to make wiser business decisions and effectively expand your company. But let’s not get ahead of ourselves—you and your team will have a lot of obstacles to overcome before you can use data analytics to guide your choices.

According to our observations, the main difficulty most business owners encounter is persuading staff members that analytics can, in fact, be useful for their employment.

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The main cause of this is a lack of expertise and training in the area. Employees frequently think that if they use analytics, they will be doing something incorrectly, and in certain situations, they may be correct.

The use of analytical thinking should never be discouraged, but you must let your staff know the various benefits that data can provide.

Data Analytics Evolution


The adage “once you know, you don’t know” couldn’t be more accurate when referring to data analytics.

You will always learn something new about data and how it can be used in your organisation, regardless of how much experience you have with data analysis.

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This is one of the reasons why data analytics is so beneficial to businesses.

Owners of businesses are optimistic that they can use data analytics to enhance business operations.

This may seem discouraging, but it doesn’t have to be. You can still use data analytics even if you don’t understand how to use it to its fullest capacity.

With a little education and research, you can improve your decision-making abilities significantly and know exactly what to do with the useful info you’re gathering.

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You could even be able to use data analytics to improve parts of your company’s operations that you weren’t even aware needed it.

Here are a few things you should think about doing if you want to use data analytics for your benefit:

Get The Required Information Get-The-Required-Information 

Adequate data must be provided for effective data analysis. There are important platforms to utilise in collecting data for business analytics, such as enterprise resource planning (ERP) systems, business intelligence (BI) apps, and others.

You can access, download, and analyse data from a variety of sources, including internal systems and other services, with the aid of the tools that are offered.

You might even quickly include data using data analytics tools from your existing systems, like Microsoft Excel or SaaS applications like HubSpot or Salesforce.

Data preparation is typically the first stage in any subsequent research effort.

You Must Choose Your Data Wisely

You-Must-Choose-Your-Data Wisely

The quality of data analytics is influenced by the information that was utilised to develop it. This covers elements like the data’s quality and the factors you take into account while evaluating it.

If you are familiar with the characteristics of high-quality data, it will be simpler for you to choose the type of data to use in your analytics project.

Stabilise the situation


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Many organisational processes need to be balanced in order to be as effective as possible. Sometimes it is important to make trade-offs that might be bad for your business.

You must strike the optimal balance between the capacity for informed decision-making and the availability of sufficient resources for business expansion.


Regardless of size, the majority of organisations make a lot of crucial decisions every day. Simple choices include choosing goods to stock or whether or not to offer a given product at a discount.

The appropriate decision may not always be so simple, and it may be necessary to conduct some research or analysis.

By giving you the knowledge you need to make wiser decisions, data analytics can be of assistance. If the distribution is a crucial component of your firm, for instance, you might decide to investigate the most effective days of the week to schedule your deliveries.

To make sure that it makes the most of your resources and results in on-time deliveries, you may then apply this data to the entire delivery process.

Data analytics is so valuable because it allows you to have all of this information at your fingertips. It doesn’t need to be challenging.

Just have the motivation to put it to use. When you do, you’ll realize that all of the efforts were worthwhile. But keep in mind to go slowly at first so you don’t exhaust yourself and make mistakes.

Making more complex decisions and conducting in-depth data analysis is possible once you have gained experience and confidence.

Companies today cannot function without websites and those that do typically succeed. Analysing client data, improving conversion rates, and improving the customer experience are the objectives.

It all starts with a straightforward choice:

Should I build a website?

The goal of customer data analysis is to increase conversion rates and customer satisfaction.

Establish the Proper Data Analysis Environment


The next stage is to build an analytical environment around your data once you have it. Here, you’ll put the data analysis tools you’ve chosen into practice, set up the tasks and timetables for the analysis, and incorporate them into your current workflow.

The ideal analytical environment will have an analyst, a data preparation team, a reporting tool, and a location to keep the derived results. These components are all required to get the most out of your data.

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It might be difficult to set up an analytical environment, especially if you operate remotely or are new to the profession.

You should form a task team to plan and carry out the process so that you are sure to have all the resources available to complete your analysis.

Individuals with experience in each field of data analytics should be included on this team, including but not limited to software development, business intelligence, and reporting.

You might also wish to involve subject-matter specialists from the disciplines of marketing, sales, and operations, depending on the toolset you’ve chosen.

Choose the Proper Data Analytics Tool


Your organisation’s technical infrastructure and your current skill set will determine the technologies you utilise for data analysis.

Software developers have the option of using a SaaS service like Tableau or Snowflake or implementing an internal solution. Examine the tool’s assistance with data preparation and integration with other software programs (such as Microsoft Excel or MySQL).

To benefit from the enormous volumes of data that these platforms can analyse, you can choose to employ open-source technologies like Hadoop or Spark if you have familiarity with operating systems and a background in analytics.

In terms of success for various markets, goods, or services can all be easily investigated using these tools, and they may be expanded to accommodate larger data sets.

If you lack the internal experience or technological foundation requisite to enable a data analytics solution, you might want to consider outsourcing the process to experts.



Your organization may start creating a pipeline for data analysis as soon as the appropriate analytical environment has been developed.

The roles and responsibilities that each team member will be responsible for carrying out must be clearly defined while building a data analysis pipeline.

Prior to defining the project’s scope, decide how much time and effort you’ll devote to it. You can use this to estimate how long it will take to complete.

It is advisable to take into account a project schedule that is divided into separate tasks, such as setting KPIs, collecting data, and performing data analysis. If there is a clearly defined process, your data will frequently be used as the basis for analysis.

Bring Dashboards and Reports Together


Every project involving data analysis must include reporting and dashboard building. With the aid of a well-designed reporting and dashboard, you can easily keep track of the progress of your project and depict your findings in an intelligible way. To create these graphics, you can utilise dashboards that work with your existing software and tools or separate reporting solutions like Tableau or Power BI.

Business intelligence (BI) tools, enterprise resource planning (ERP) systems, and other sources are just a few of the data sources that these technologies can integrate in order to create dashboards that present vital data in an intelligible manner.

Keeping Data Analytics Results


As the digital economy changes, businesses are saving more and more data—both structured and unstructured.

You’ll be able to rapidly obtain numerous details regarding the previous performance or just assess the status of your projects by appropriately preserving and utilising your data.

You’ll require a location to keep your results once your data analysis job is finished. Depending on the size of your data set, you might choose to either store your findings in a relational database or a functional data store like MongoDB or Elasticsearch.

Make a data dictionary that lists the names and descriptions of the necessary fields for each type of analysis you undertake in order to make sure that your data is properly indexed and simple to retrieve.

It’s time to change your approach if you are unfamiliar with data analysis or if the procedure looks onerous.

Although putting into practice a data analytics program can be difficult, it is a required expense for conducting business in the modern world.

Your confidence will unquestionably rise and you’ll be able to make better judgments for your business by utilising the tools, methodologies, and professionals to extract insightful information from your data.


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About the Author

Tom Koh

Tom is the CEO and Principal Consultant of MediaOne, a leading digital marketing agency. He has consulted for MNCs like Canon, Maybank, Capitaland, SingTel, ST Engineering, WWF, Cambridge University, as well as Government organisations like Enterprise Singapore, Ministry of Law, National Galleries, NTUC, e2i, SingHealth. His articles are published and referenced in CNA, Straits Times, MoneyFM, Financial Times, Yahoo! Finance, Hubspot, Zendesk, CIO Advisor.


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