Did you know that data can open up countless opportunities for your business to thrive?

In today’s fast-paced digital landscape, data analytics is not just a buzzword, it’s a crucial tool for driving business value.

But what is the role of data analytics?

It’s more than just having access to data. It’s about empowering your business and ensure sustainability with the right information, tools, and resources to analyze data and extract meaningful insights.

According to recent reports, 73.5% of respondents in data-leading companies across the globe reported that their decision-making processes were always data-driven.

This statistic underscores the importance of embracing data analytics as a driving force behind a business’s success.

So, are you ready to unlock the full potential of your data? Read on!

How can data analytics contribute to business value?

Let’s break down 8 practical ways data analytics can add value to your business:

• Business Goal Alignment
• Productivity Enhancement
• Problem-Solving
• Better Data Management
• Faster Communication
• Growth Opportunities
• Data Monetization
• Personalization

Dive into the advantages and learn strategies to attain them:

1. Setting Clear Goals: Measuring Success with KPIs

Data analytics has revolutionized every facet of business and key performance indicators (KPIs) are no exception. In the past, KPIs mainly focused on sales and revenue metrics, but now they encompass a wider range of factors in your business, like clicks, customer visits, average user session duration, lead conversion rates, and more.

The emergence of Artificial Intelligence (AI) models has introduced new KPIs tailored to measure the performance of these models specifically. These analytical measurements align with your business objectives rather than relying on standard metrics to achieve desired outcomes. Some data analytics business growth examples are shared for your reference.

For instance, Amazon utilizes metrics like Weighted Absolute Percentage Error (WAPE) to gauge the accuracy of forecasts compared to the actual outcomes for a given sample. This use of data analytics ensures that KPIs are finely tuned to reflect the real-world impact and effectiveness of business strategies and AI technologies.

2. Enhance Productivity

Data analytics is your strategy for making smarter decisions. By diving into the data, leaders can craft strategies backed by solid evidence, really understand their customers to fine-tune marketing efforts, and, ultimately, boost productivity.

Analyzing big data analytics market can help you spot trends and patterns. With that knowledge in hand, you can tailor your products or services to meet exactly what your customers are looking for. And the more you know about your customers, the better you can build loyalty andramp up sales.

So, data analytics isn’t just about numbers; it’s about knowing your customers inside and out and using that knowledge to fuel your business growth.

3. Reduce Problems

Data analytics can assist in minimizing business challenges. With analytics, you understand exactly who your customers are and what they want, making your marketing efforts effective.

It helps in customer segmentationby sorting your customers into groups based on who they are and what they like. In revenue forecasting, analytics can help you mitigate problems, giving you a clearer view of where your business is headed financially.

Analytics can pinpoint where you’re overspending or where there’s room for efficiency improvements, helping you save those precious dollars.

Data analytics helps streamline your processes and activities for maximum efficiency and quality. We all face uncertainties, but with analytics, you can identify and tackle potential threats head-on, keeping your business safe and sound.

For example, the American Express Data Analytics, Risk, and Technology (DART) Laboratory helps to tackle risks that come from how people behave and the decisions they make. The main aim of the lab is to deliver tech solutions that help us manage business risks better and help in crisis management.

4. Reduction of Duplicate Data

Data duplication is a big hurdle when you’re trying to analyze data accurately and efficiently. Data analytics and AI can help sniff out and eliminate duplicate data in those massive datasets you’re dealing with. Not only does this save you storage space, but it also turbocharges your data processing speed.

An example of data analytics to drive business value is Natural Language Processing or NLP. It helps you analyze unstructured data like text to spot possible duplicates based on context and meaning.

Take Microsoft, for example. They’re using NLP for all sentiment analysis, figuring out topics, detecting languages, pulling out key phrases, and sorting documents into categories.

5. Streamline Processes by Identifying Experts

Did you know that advanced analytics can help us find the ideal person to solve a specific question or issue?

Let me give you an example. CData Software, a platform for assessing data, uses advanced analytics to pinpoint the experts in their support team. They track how many tickets each support representative handles using Atlassian Jira. They also organize the tickets by the products or services customers are asking about.

Then, their sales team takes this data for business analytics and puts it into reports and dashboards. This helps them figure out which support representative is good to contact when a potential customer has a technical question about a certain product, feature, or function.

This system has made teamwork within the company better and has left customers happier with the support they receive.

6. Unlock New Opportunities

With advanced data analytics, businesses can easily predict what customers want before they even know it themselves. By digging deep into data, businesses can spot hidden patterns, see trends before they happen, and even forecast future outcomes with impressive accuracy.

For example, Avionos is an expert in digital consultancy and uses advanced analytics to help its clients uncover exciting new opportunities. By diving into customer behavior, global data analytics market size, trends, and more, they can pinpoint what resonates with customers.

7. Data Monetization

A lot of companies out there have valuable information they can use to make money through advanced analysis. Uses of data analytics make them find hidden treasures in your data that others would pay for. Some data analytics business growth examples are discussed in the underlying sections.

For example, Katalys has rolled out a new solution called Katalys Automated Monetization (K.A.M). It’s designed to help publishers boost revenue and allow brandsto expand their customer base.

The key is to find a platform that allows you to share data easily and supports advanced analytics. This way, you can maximize the value of your data by pinpointing changes that can lead to better outcomes.

8. Personalizing Experiences

Advanced analytics is important to personalize customer experience. It helps to enhance customer satisfaction and engagement. For example, Sports Buff teamed up with Ensono, a tech partner, to make watching sports even better. During FIFA World Cup Qatar 2022, they added cool graphics to the broadcast, giving fans extra information and ways to get involved.

They collected data from different places about what fans like and don’t like, then used that information to make sure everyone gets a personalized experience.

In today’s business landscape, many companies have huge sets of data, yet only a handful know how to truly tap into its potential. This advantage doesn’t just mean better investment outcomes; it can mean surpassing expectations altogether.

Let’s take a look at how data analytics influences the investment journey!

What is the Impact of Data Analytics on the Investment Lifecycle?

Data analytics plays a pivotal role in every stage of the investment process, aiding companies in achieving Return on Investment (ROI) and optimizing returns. It helps in the following:

• Pre-Acquisition Analysis: Before acquiring a company, investors rely on data analytics to forecast post-acquisition revenue potential. Visibility into existing revenue and pipeline data helps estimate the revenue runway after acquisition.
• Examining Cost Structures: Besides revenue analysis, evaluating the cost structure is equally vital in pre-acquisition analytics. Transitioning into the entry period requires a deeper level of data analysis for both revenue and costs.

Data analytics industry trends are here to drive future sustainability. Limited pre-acquisition data emphasizes the significance of data analysis during the investment and growth phases. Compiling portfolio data offers a comprehensive view, unveiling opportunities for maximizing ROI.

For example, analyzing a portfolio company’s data assets can reveal valuable insights. These are:

1. Understanding Your Business: It will take a closer look at what’s happening with your top-selling products or services. If sales are lower than expected, you might need to adjust your marketing strategies to give them a boost.
2. Identifying Your Ideal Customers: The analysis can pinpoint common characteristics among your most valuable customers. This helps create customer segments and ideal customer profiles (ICPs), so you can tailor your efforts to attract more of these high-value customers.
3. Streamlining Your Operations: It digs into your data to find out if certain products are costing you more to produce or manage. Understanding the relationship between costs and revenue for these products can identify areas where you can cut waste and optimize efficiency.

According to recent insights, using AI can help you strategize financial policies. It means you gather insights from data to formulate investment strategies and create personalized products that fit your needs.

In summary, data analytics serves as a cornerstone in investment decision-making, offering actionable insights across various stages of the investment lifecycle.

Now read about data analytics industry overview in the following sections.

Role of Data Analytics to Drive Value and Maximize ROI

Data Analytics Industry Growth

When you’re steering your company culture towards making decisions backed by data, it’s smart to start by understanding how data can boost value and investments. Businesses need to pinpoint which data points are the most valuable.

Both Private Equity (PE) firms and the leaders of the companies need to have ongoing access to the following regularly updated information:

1. Clear Product and Service Index: You need a straightforward list of everything your company offers. Each item should have its own code or description so you can easily tell them apart. This helps in understanding their prices and value better. If things are grouped together, it’s hard to see what each one brings to the table individually.
2. Central Customer Database: It’s crucial to have one big list of all your customers. And if you have their info in different places, it’s essential to use the same codes or descriptions for them everywhere. This also goes for how you name businesses across different systems. Using different names for the same thing can mess up your data and make analysis harder.
3. Additional Lists as Needed: Depending on how your business works, you might need other lists too. These could include things like lists of buildings, employees, different cost areas, or how your departments are set up.
4. Company-wide Sales Tracking: You should always have access to updated lists showing all your sales. It’s helpful to break these down by things like region or product type. This makes it easier to see how different parts of your business are doing.
5. Understanding All Business Costs: It’s not just about how much money you make from selling something. You also need to know how much it costs to make or deliver it. Sometimes, regular accounting systems don’t give the full picture of how much you’re really earning from something. So, it’s important to track all the costs involved.

When it comes to tracking and understanding your data, there are a bunch of tools out there to help you visualize, analyze, and stay organized. But, with so many options, it can be tough to figure out which one fits your needs best.

Let’s dive in and find some of the best tools!

4 Best AI Data Analysis Tools in 2024 and 2025

Some data analytics industry trends and tools for you to explore are discussed in the underlying section.

1. Microsoft Power BI

Microsoft Power BI is a popular tool for business individuals. It helps them properly make sense of their data. You can bring in data from all sorts of places and then create reports and dashboards to visualize it, which can give you some really neat insights.

Additionally, you can also use it to build machine learning models and tap into some other smart features to help you analyze your data even more.

Power BI comes well with other integrations like Excel. It isresponsive and easy to use. However, it can be pricey compared to some other options out there.

Curious about mastering data visualization for the industry? Keep reading!

2. Microsoft Excel

Microsoft Excel has been a top-notch tool for data analysis since it first came out, and it’s still holding its own today.

With Excel’s Data Analysis Toolpak, you can dive into your data and make different charts and graphs to understand it better. It filters data quickly using search boxes and pivot tables.

Additionally, you can get into making machine learning models without writting a single line of code, using formulas to analyze data in different ways, and even building complex neural networks.

However, Excel does have its limits. It’s built around the idea of spreadsheets, which can take a bit of time to get the hang of all its features, especially if you’re not into coding.

3. Akkio

Akkio is a good tool for business individuals who want to do dataanalysis and data forecasting. With Akkio, you don’t need to do coding to dive into your data. It is lead scoring, which helps you figure out which leads are worth chasing after.

Akkio helps you predict future trends based on your data. Plus, it’s easy to use and connects seamlessly with other tools you might already be using. So, you can import and export data easily.

For further insight into Akkio, learn more!

4. Qlik

QlikView is a data visualization and business intelligence tool. Itallows AI technology to help you analyze your data.

One of the things individuals like about QlikView is its user-friendly setup. It’s a good tool for teamwork. You can collaborate with your team seamlessly thanks to features like drag-and-drop editing and easy workflows.

While QlikView is a platform with good functionality, it’s important to consider a couple of things. Firstly, it does come with a higher price tag compared to some alternatives. Secondly, its AI features might not be as extensive as some other options out there.

Synopsis

How do you make your organizations foster engagement and embrace a data-driven culture? This blog offers 8 essential strategies to leverage data analytics and elevate your business’s value.

By prioritizing data in your decision-making processes, your organization can uncover numerous opportunities to create value for everyone involved. Here, we have discussed the pivotal role of data analytics across the investment lifecycle and its profound impact on maximizing Return On Investment (ROI).

With a dedicated emphasis on data in decision-making processes, businesses unlock opportunities to generate value for all involved parties. Here, we have discussed the role of data analytics in the investment lifecycle and in maximizing ROI.

Furthermore, we provide a comprehensive overview of popular data analytics tools, like Microsoft Excel, Microsoft Power BI, Qlik, and Akkio, to empower your data-driven initiatives.

Grab the data-driven strategies and explore the best programming tools for 2025 to unlock the full potential of your organization!

References

1. Belghith, M., Ben Ammar, H., Masmoudi, F. and Elloumi, A., 2021, December. Data Visualization for Industry 4.0: Developing Dashboards with Power BI–A Case Study in a Pharmaceutical Company. In International Conference Design and Modeling of Mechanical Systems(pp. 402-408). Cham: Springer International Publishing.

Link: https://link.springer.com/chapter/10.1007/978-3-031-14615-2_45

2. Dong, J.Q. and Yang, C.H., 2020. Business value of big data analytics: A systems-theoretic approach and empirical test. Information & Management.

Link: https://www.sciencedirect.com/science/article/abs/pii/S0378720617308856

3. Bordeleau, F.E., Mosconi, E. and de Santa-Eulalia, L.A., 2020. Business intelligence and analytics value creation in Industry 4.0: a multiple case study in manufacturing medium enterprises.

Link: https://www.tandfonline.com/doi/abs/10.1080/09537287.2019.1631458

4. Ciampi, F., Demi, S., Magrini, A., Marzi, G. and Papa, A., 2021. Exploring the impact of big data analytics capabilities on business model innovation: The mediating role of entrepreneurial orientation.

Link: https://www.sciencedirect.com/science/article/abs/pii/S0148296320306068

5. Hallikainen, H., Savimäki, E. and Laukkanen, T., 2020. Fostering B2B sales with customer big data analytics.

Link: https://www.sciencedirect.com/science/article/pii/S0019850118304656

Since its invention back in the 1990s, digitization has diffused its way into every conceivable sector of commerce; specially the service industry. In financial services the rise of digital technologies has dramatically changed the manner in which institutions deliver information to their clients. Zivanta Analytics has helped in data digitization of a leading real estate hedge fund company which has brought more efficiency in their work there by shortening the TAT for their bidding for each deal.

The company’s primary objective is to deploy capital for pools of real estate assets that contain a title, document or compliance issue which impacts the marketability of that asset to the current owner. It utilized an asset based due diligence review which encompassed the legal curative strategy, property value and cash flow analysis data points needed to secure the assets with an appropriate equity position to protect a potential investment risk.

The objective of the company is to acquire real estate pool which came with thousands of scanned collateral documents which told the story about the constituent properties, its lien and title status, default details, assignments and other key variables which determine the risk profile of the property. For taking a price position for bidding for a Deal, the risk profile needs to be determined.

To create the risk profile of these properties, the relevant data had to be culled out from the collateral documents for each property. Zivanta Analytics had been retained by the Hedge fund company to sift through thousands of collateral documents and the key indicators. Zivanta Analytics has also built an analytics engine which uses the key data to build the risk contours of each individual property in a deal.

The company selected a specific deal and had transferred the entire collateral set to Zivanta Analytics in pdf format. Analysts at Zivanta analytics who are trained to look into the real estate collaterals, sorted the documents and looked at the completeness for building the collateral datasets. Accordingly the digitization strategy for a given deal was decided.  The collaterals which could be machine read were sent to the technical data scraping team who wrote codes to digitise. The remaining collaterals which were not machine readable were sent to the data associates who keyed in the data for each property from each collateral using either voice or key board based data entry method. As the data were entered, the data quality team concurrently checked the  data for quality using proprietary checking  tools and analytics.

The cleansed data was then processed by the Zivanta proprietary analytics engine to create a risk profile for each property.  The data could be queried by the hedge fund company experts through a custom interface developed by Zivanta Analytics. A dashboard to help analyze the risk profile was also developed.

With the help of this newly developed process the hedge fund company got quality data on tight deadlines at very economic costs. The risk profile created using the data from the collateral documents helped make an informed decision regarding the bid price for acquiring a deal. Once a Deal is acquired, the  experts  at the company are now using the data set to identify and mitigate the risk associated with each individual property  thereby making the property marketable at prevailing  market rates.

The 80s and the 90s made a revolution in the banking industry when IT systems virtually revamped the whole banking process. The use of the internet  made it a lot easier to assess and evaluate the progress of a bank beforehand. It even had enhanced the service provided by the banks.

Banks always have a lot of information regarding their clients. With this information they can learn newer ways to provide better services. With the insights they have, they can make sure their customers are always provided with resources that are beneficial and not available elsewhere. The use of Big data has now opened up a new way for banks to be more profitable. The very use of Big data is enabling the banking sector answer a simple question in seconds which is not possible without harnessing the power of Big Data.

Big Data is an extensive system that can help banking industries simplify their system and work better. It helps them in developing a sincere clientele. Following is a list of ways in which the banking industry is affected by the use of Big Data:

Customer Segregation:

When a bank is provided with the insight to track and trace the habits of their clients of where and how they spend their money, it becomes easier for them to understand the clients’ needs. Once the results are analysed, clients can be categorized into different segments. This will suggest their banking needs and the marketing campaigns can be drawn up accordingly. This will also promote a healthier customer relationship.

Cross Sell Opportunity Enhancement:

With the use of Big Data, the banking industry can improve their relationships with their clients and understand them better. Big Data provides a more personal assistance and helps banks with detailed information on their clients. This information helps banks come up with schemes that are direct and personalized. These schemes gain maximum attention and are able to make sure they can come up with similar plans for the future. As their ability to analyze and assess the situation increases, their ability to deliver according to the expectations of their clients increases as well.

Efficiency Improvement:

Big Data helps banks avoid all situations that can be embarrassing for them. When a client asks a question that can only be answered by assessing the database, Big Data comes handy. The clients no longer have to wait to be answered because of a system failure. Their queries are solved immediately. By entering the name of the customer, banks are provided with all his important details. This also increases the level of satisfaction received from the customers. It also develops a sincere number of clients that learn to rely on their banks more.

 Fraud Detection:

Internet no doubt has made life simpler; however, it has also made fraud easy. When banks come up with newer ways to stop fraud, people come up with newer ways to commit it. This makes it impossible for banks to be able to trace the fraudulent convicts. The use of Big Data rids them of this problem. It makes it easy for banks to immediately be able to trace a fraud and put an end to it. The installation and use of this system makes it easy for banks to identify where the fraud is being committed. They can stop it immediately without delaying or experiencing a loss.

Risk Management:

With help of Big Data information is easily located on a single large scale platform and it becomes easier to reduce the number of risks. Everything that is needed by the banks becomes available at a central platform. This reduces the chances of them losing any information. It also helps them avoid being ignorant towards a fraud. They can easily detect them and in turn reduce all kinds of risks.

So in a nut shell Big Data is very beneficial. If it is taken up by industries it can be utilized with the right kind of knowledge. It assists the banks in taking steps and coming up with initiatives that provide guaranteed benefit. Big Data not only helps banks attract a larger client base but also helps them markedly reduce all frauds and secure their system.