Implementing Big Data

Modern-day organisations capture significantly more data than ever before. This comes from a wide range of sources and offers companies huge potential across a range of business activities, such as product development, operational efficiencies and customer experience.

Despite this, making the most of your data opportunities can be very challenging. Many finance teams struggle to capture, process and report data in the most valuable way for your company.

In this blog post, we will discuss what Big Data is, why it is so important, some of its main challenges and provide practical tips for CFOs that can help businesses to use Big Data to improve their bottom line.

Definition

The definition of Big Data can be tricky because it means different things to different people. From a CFO’s perspective, Big Data refers to the large volume of data that is being collected and processed by organizations.

It can be difficult for businesses to keep up with this influx of data, which typically leads to data sets that are so large or complex that traditional data processing applications are inadequate. However, those companies who can harness it will be able to gain a competitive advantage.

Main benefits

Organizations that have embraced Big Data are able to make better decisions, faster. By analyzing large data sets, they can identify trends and patterns that would have otherwise gone unnoticed. This allows them to be more proactive in their decision-making, rather than reactive.

Additionally, Big Data can help organizations save money. For example, by analyzing customer purchase data, a company can identify which products are selling well and adjust their inventory accordingly. This can help them avoid over-ordering or under-ordering, both of which can lead to significant losses.

In fact, Big Data provides huge opportunities for companies across most of their activities. Here are five examples:

1. Product development: Big Data can help companies to understand customer needs, track customer trends, and identify preferences. This information can be used to improve existing products or to develop new products that better meet customer needs.
2. Customer experience: Big Data can help companies improve their customer service by understanding customer behaviour and feedback. Data can be used to identify customer pain points and areas for improvement. By understanding what customers want and need, companies can create a better overall experience that leads to loyalty and repeat business.
3. Operational efficiency: Big Data can help companies improve their operational efficiency by identifying inefficiencies and areas for improvement. Data can be used to streamline processes and improve coordination between different departments. By understanding where bottlenecks occur, companies can make changes that lead to greater efficiency and productivity.
4. Driving innovation: Big Data can help companies drive innovation by identifying market trends and new growth opportunities. Companies can use data to track industry performance and spot potential areas for expansion.
5. Forecast performance: Big Data can be used to create projections about future business activity by understanding past performance and trends, both within your business and also across the wider marketplace. Companies can use data to plan for upcoming demand, set goals, and allocate resources accordingly. By understanding what the future may hold, companies can be better prepared to meet the challenges and capitalize on the opportunities that lie ahead. 

Big Data is a powerful tool that offers huge benefits to companies across all industries. Organizations that embrace this technology can reap significant rewards.

The seven Vs

The requirements of Big Data have expanded significantly since the term was first coined. The modern-day characteristics of Big Data are best illustrated by the seven Vs:

1. Volume: The amount of data being generated is increasing exponentially. For example, Facebook generates over 500 terabytes of data per day!
2. Velocity: The speed at which data is being generated is also increasing. For example, Twitter users generate over 400 million tweets per day!
3. Veracity: The accuracy of data is often uncertain. For example, data from social media is often unstructured and can be opinionated.
4. Variety: There are many different types of data. For example, textual data, numerical data, images, videos, etc.
5. Value: Not all data is created equal. Some data is more valuable than others. For example, weather data can be very valuable to farmers.
6. Virtualization: Data can be stored and accessed virtually. For example, you can store data in the cloud or on a virtual machine.
7. Visibility: The ability to access and analyze data is becoming increasingly important. For example, business intelligence tools allow you to visualize data and make better decisions.

As you can see, the seven Vs of Big Data are very important factors to consider when working with data. Finance teams should consider each of these factors to determine how your business can make the most of Big Data.

Key challenges

The key challenges of implementing Big Data are threefold: first, the volume of data is often too large for traditional relational databases to handle; second, the data is often unstructured or semi-structured, making it difficult to query with SQL; and third, the data may be coming from a variety of sources, making it hard to integrate into a single system.

Many companies hold data in a mixture of legacy hosted and online systems that is captured from different sources, stored in various formats, and divided into siloes. These systems may struggle to handle the increased volume of data and are often no longer supported by their creators. Many rely upon manual workarounds for reporting, such as extracting data into Microsoft Excel, and are difficult to integrate with newer technologies. Processing data can be inefficient and tricky to cut into different dimensions.

While there are many potential benefits to using Big Data, the challenges of implementation should not be underestimated. Organizations need to carefully consider their specific needs and objectives before embarking on a Big Data project, and they need to have the right infrastructure and expertise in place to make it successful. With careful planning and execution, however, the rewards of Big Data can be great.

Data sources

There are many different types of data sources that can be used to collect Big Data. Some common examples include social media data, web server log files, sensor data, and financial transaction records. In general, any type of digital information can be a source of Big Data. To get the most value out of Big Data, it is important to choose data sources that are relevant to the problem you are trying to solve. For example, if you want to use big data to improve customer service, then you would want to collect data from sources like call centre records and online customer reviews.

It can be difficult to know where to start when collecting Big Data. However, there are a few common data sources that are used more often than others. Social media platforms like Twitter and Facebook generate a huge amount of data every day. Web server log files can also provide a lot of useful information, such as what pages are being accessed and how long visitors are staying on each page. Sensor data is another type of Big Data that is becoming increasingly important as the Internet of Things grows. Financial transaction records are also a common source of Big Data, especially for businesses that want to use it for fraud detection or marketing purposes.

Collecting big data can be a challenge, but it can be extremely beneficial for businesses and organizations. By understanding the most common types of data sources, you can make sure that you are collecting the right data for your needs. With the right data, you can solve problems more efficiently, improve customer service, and make better decisions. Choose your data sources wisely and you will be well on your way to unlocking the power of Big Data.

The role of finance

Finance has become the latest industry to jump on the Big Data bandwagon. From banks to insurance companies to coffee shop chains, everyone is looking for ways to harness the power of Big Data to improve their bottom line.

Finance’s role in Big Data is two-fold: first, to provide the funding necessary to support Big Data initiatives; and second, to make use of the data itself to drive better decision-making.

Big Data is expensive. The hardware, software, and human resources required to collect, process, and analyze large data sets can quickly add up. That’s why many organizations turn to finance for help in funding their Big Data projects.

But it’s not enough just to provide the money. Finance also needs to be involved in using the data to make better decisions. This involves strong collaboration with the wider firm to efficiently capture, process, integrate, analyze and report data in a meaningful way that generates valuable insights to its stakeholders and end users.

By working closely with other departments, finance can provide the funding necessary to support Big Data initiatives while also using the data to drive better decision-making.

Implementing Big Data

First, CFOs need to understand what Big Data is and why it’s important. They need to be able to articulate the business case for Big Data to the rest of the executive team and build consensus on its value.

Once they have buy-in from the other executives, CFOs need to work with their IT team to develop a Big Data strategy. This strategy should identify the specific business problems that Big Data can help solve, and how it will be used to make better decisions.

CFOs also need to put in place the right organizational structure and governance framework to support a successful Big Data initiative. This includes establishing clear roles and responsibilities for those involved, as well as setting up processes for data collection, storage, and analysis.

Finally, CFOs need to ensure that they have the right tools and technologies in place to support their Big Data initiative. This includes investing in data management and analytics platforms that can handle large volumes of data.

Once this has been established, here are seven steps to help CFOs implement Big Data into their business and maximise its benefits to the organisation:

• Beneficial data review: The first step is to review all the data that the company has been collecting. The CFO needs to determine what data is beneficial to the business and what data can be discarded. This is essential to avoid spending lots of time and effort on tracking data that nobody cares about.
• Identified sources: The next step is to identify where the data is coming from. There are many sources of data, including internal systems, social media, financial reports, customer surveys, and website analytics. For all beneficial data, these systems need to be reviewed to determine how easy it is to integrate the data flows and format the data. 
• Global storage hubs: Companies need to create global storage hubs to store all the data. Each storage hub must be a trusted secure platform that can integrate with each relevant system. It may also be necessary to create data lakes that can store both structured and unstructured data.
• Data source connection: The next step is to connect the data sources so that you can collect and process each category of selected data. This will allow the CFO to see all the data in one place. Such connections may include third-party systems such as customers, clients and social media platforms, in addition to internal company systems.
• Analysis of data: Performing data analytics helps to determine what the data means and how it can be used to improve the business. This will involve tracking key metrics, analysing trends, and explaining variances. Graphs and tables will help to summarize data for the end user and allow finance to link the underlying performance data to the company’s commercial activities.
• Testing procedures: The CFO needs to make sure that the data is accurate and that the procedures are working correctly. This may involve spot-checking data, performing reconciliations, and creating automated error alerts for unexpected differences.
• Automated reports: The seventh step is to create automated reports. The CFO needs to be able to provide the data to each stakeholder in a clear and concise manner. Such reports should highlight the most relevant data to each party and be delivered in a well-structured format to generate helpful insights across multiple dimensions.

By taking these steps, CFOs can set their business up for success with Big Data. And with the right data in hand, they’ll be able to make better decisions that drive growth and profitability.

Summary

Big Data is a big topic in the business world today and CFOs need to take advantage of this. Many companies are struggling to keep up with the ever-changing landscape of data. By properly understanding this, CFOs will be able to implement Big Data in their business and make better use of data to inform their decision-making.