27% of businesses state that data-driven projects are already profitable – How to turn data into actionable intelligence?

Published On September 28, 2020
In Technology, Data Analytics, BI & Visualization, Blog Archives

In the tech-driven world that we live in presently, there’s no doubt that data plays a major role in helping businesses advance to the next level. However, there are various types of data that is created in various different formats. Right from structured formats to unstructured and even semi-structured format, data exists in multiple formats and types.

That being said, while companies would prefer possessing structured data that is straightforward and easy to understand, the presence of data in the unstructured format is far more common.

Unstructured data is extremely difficult to understand and derive insights from, hence, the abundance of data in this format creates multiple challenges for businesses. This is true especially for those companies that are hoping to leverage the Internet of Things (IoT) as a primary source for collecting data.

It’s imperative for companies to be able to structure data so that they can derive insights that are valuable and cost-effective. Hence, if you don’t have a functional solution for analysing and using unstructured data, you’re not making the most of the data available to you.

In this blog, we will cover the difference between the different types of data and how you can extract invaluable insights from the data.

An introduction to business data

Structured data

Structured data is often referred to as quantitative data, consisting mostly of objective components, for example a number or email address. This simply means that when information is structured, the data can be formatted in a traditional database format and in distinct categories, where it can be easily understood and used to discover insights. It is well-suited for storage in relational databases such as SQL, since structured data is clearly defined.

Most legacy data processing systems are designed to interact with relational databases. When information is ordered and labelled in a tabular format, it is usually simple to manipulate and analyse the data for an automated computer operation. In various combinations, queries will pull data from tables, easily correlating different field names and types of data to uncover patterns that translate into useful insights.

Structured data, which is widely used for anything from web page creation to business analytics, should be familiar to any programmer. From purchases and reservations to inventory and GPS, an array of basic business processes produce or depend on structured data. The following objects, for example, may easily be arranged within a relational database as interrelated items:

  • Email Addresses
  • ZIP Codes
  • Addresses
  • Names
Types of big data | SG Analytics

Unstructured Data

There is no particular format for unstructured data and it is not created for a conventional relational organisation. Basically, it can’t logically exist in a row-column format database and traditional methods cannot be used to format, understand and study this data. Hence, you can think of it as “subjective” information.

There are increasingly common unstructured data formats that include the following:

  • Photos, Audios & Videos: This type of data cannot mould into the constraints of a standard database schema, although they are popular. However, it’s crucial to analyse this data as its highly effective insights can be derived.
  • Text files/PDFs: This data is not easy to read by machines within otherwise organised records As they have some metadata, emails and websites may be called semi-structured, but again, it is not possible to easily decipher the text and message fields.
  • Communications: Popular examples of this data are Chat logs, transcripts, and even messages and comments from social media. All the information is unstructured since the “material” evades reading by the analytical computer.
  • IoT: A variety of data types is generated by heterogeneous sensors. While the data generated is not inherently unstructured, it is mostly distributed in a specific format, making it unsuitable for storing relational databases.

The difference between data, information, and insights

Before you can use data effectively, it’s important to understand important terminology associated to data analytics. Hence, we’ve defined three of the most crucial elements in this process; data, information and insights.

  • Data:

Data comprises facts that are not processed or categorised and are recorded according to some agreed-upon criteria. A number, a picture, an audio clip, a transcription, or the likes can be described as data.

  • Information:

Information is data that has been translated, aggregated and arranged into a format that is more human-friendly. Visualizations, graphs and dashboards of data are popular ways to display data.

  • Insights:

In order to understand the underlying meaning of a particular fact or data pointer, and to derive upon certain actionable pointers, insight is obtained by analysing data. These action points need to be executed in your business processes to ensure they can accelerate your growth.

The impact of insight on business decisions

Today’s brands have access to infinite data sources. But a stack of numbers doesn’t really tell you something important about your business. Insight does.

The business benefits of data analysis

Increase productivity

Applying analytics and optimising company processes to design and monitor the process ensures quality and productivity to fulfil consumer expectations and achieve operational excellence. In order to boost field operations, productivity , and performance, as well as manage the workforce of the company according to business needs and customer demand, businesses may use advanced analytics techniques.

Understand what your consumer wants

The traditional approaches to performance and delivery have been modified by analytics. Organizations use their best processes to meet orders along with their resources when it comes to delivering a product. Multiple companies have been enabled by analytics to predict their ability to fulfil the needs of the customer.

Data-driven decision making | SG Analytics

Better decision making

This is truly the best advantage of data analytics. First-rate data collection and speed allow companies to perform faster and more informed operations. In highly competitive companies, that is very crucial. Multiple companies use large volumes of data to measure client experiences and their reactions. For example, analysing voice calls and identifying client satisfaction markers.

Discover new opportunities

When demand changes or new technology is developed, effective data collection, combined with analytics, helps businesses stay competitive. It also enables them to anticipate market demands to supply the item before it is requested. By seeking attractive markets, this helps companies explore new opportunities.

In a nutshell, there is no doubt that there are multiple benefits of identifying insights from data to make decisions for your business. However, according to a report published by McKinsey, the biggest barriers that companies face while extracting insights from data is organizational. Basically, many companies struggle to incorporate data-driven insights to improve their operational and day-to-day processes. Moreover, the report further stated that 40% of respondents reported a great difficulty of finding analytical talent, and that retention was a concern too.

How to turn data into actionable insights

Having the right tools and techniques to collect relevant and reliable data is great. However, that data is of no use if you cannot derive actionable market and customer insights that can be executed to enhance the performance of your company.

Here are 5 effective tips you can use to convert your data into actionable insights that can truly help take your business to the next level.

1. Measure The Right Things

If you don’t measure the right components, you cannot derive any useful information. Every business is different, hence, you cannot use the same process and solution every time. However, there are certain pointers that every sector can bank upon.

Let’s take the e-commerce industry for example. Here are some of the most important factors you should measure:

  • Channels that drive maximum conversions
  • Places where maximum customers leave your website before making a transaction
  • Devices most people use to purchase products
  • Landing pages and channels that have maximum scope of improvement

2. Ask The Right Questions

To answer all of your customer’s questions, go the extra mile. This means tapping into the expectations and problems of the client by asking the necessary questions.

Before you launch the next data analysis, it’s a must to formulate a simple business question. By getting lost in your details, you will waste crucial time. And it can also be highly frustrating to come up with “insights” that are already established or not considered significant!

Hence, it’s important to dive deep and ask the right questions to your customers to get data that is capable of providing actionable insights.

Convert data into actionable intelligence | SG Analytics

3. Segmentation Drives Action

When you want to take action on your results, go for segmentation! You can start digging deeper by grouping visitors depending on what they have in common. Selecting which segments to research depends on what decision or action point you are looking for.

Identifying segments can significantly increase the awareness of how the clients act. Moreover, with a lot of built-in segments, digital analytics tools like Google Analytics provide you with all the freedom to configure them to your requirements.

4. Visualizations are extremely effective

The manner in which you present your data will make a big difference in the result. Can you recall the presentations containing only terms and numbers? This is in contrast to simple visualisations that inspire cognition rather than uncertainty.

5. Recognise the context

Everyone has data and has their own perspective (opinion) powered by personal data. A superior interpretation of context contributes to the right choices in most situations.

Be sure to create a background for the details you see. What do they mean by these numbers? Are they meaningful? Is it really affecting the company? And how is the information collected?

Data without meaning is not that important and, because of viewing it in the wrong way, can potentially lead to bad business decisions!

Conclusion

There’s no doubt that deriving actionable insights from data is crucial for businesses. In fact, a report published by Capgemini and Informatica states that 27% of the businesses that were surveyed indicated that data-driven projects are already profitable and 45% of the respondents were already at break-even stage.

Using the right techniques and technology wisely is the key move for better digital transformation – and prioritising consumer and customer journey in your data analysis is essential for all your company and service units.

However, to ensure you’re able to make the most of your data, you need to have data analytics services and proper processes in place for effective data analysis, which will help you determine actionable pointers that can help accelerate the growth of your business!

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SGA Digital Marketing Team
SGA Digital Marketing Team
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The SG Analytics Digital Marketing team creates and optimizes content for service and industry-specific blogs. We are a dynamic team focused on building our company's brand online, as well as sharing the expertise of our subject matter experts through the medium of our thought leadership blogs.

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