Big data: what is it? Future-proof your organization

Every organization has data. The challenge is to use this data effectively (and efficiently). Because what matters is not how much data you have, but what you do with it. In this article, you'll read what data you can use to solve strategic issues, how to collect and analyze the right data, and what tools can help you do so.

Data development is moving at a rapid pace. Did you know that as much data will be collected in the next three years, as in the 30 years before that? Mind blowing, right? We are constantly generating data. Right now as much as 1.7 MB per second. And that number is growing, and growing faster and faster. Data is everywhere; for example, Google is our biggest advisor, we send dozens of emails every day, and we closely monitor our social feeds.

Every organization has data. The challenge is to use this data effectively (and efficiently). Because it doesn't matter how much data you have, but what you do with it. In this article you will read about what data you can use to solve strategic issues, how to collect and analyze the right data, and which tools can help you do so.

What is big data?

With the advent of more data, more (and larger) data sets are also emerging. Potentially a goldmine for the development of (commercial) organizations. At least...for those who are able to monetize the data to the benefit of the customer. And that is exactly where the challenge lies. Because many of these large data sets cannot be analyzed using traditional methods. They are "unstructured. These unstructured data sets are also known as "Big Data."

When we think of data, we quickly think of large amounts of text or sets of numbers, but there are many more types of data. Marketing and sales departments alone produce data in many shapes and sizes:

• Marketing costs and revenues
• Website and/or portal visitor data
• Customer communication via phone, mail, chat or whatsapp
• Quotes, invoices, contracts, etc.
• Minutes of internal/external meetings
• A map with the location of all customers
• Photos of events, etc.

Data has various manifestations, such as documents, flowcharts, figures, photos, web analyses, dashboards, etc. Big data is about the total sum of available data, where you combine different sources to arrive at valuable insights. Like a 360-degree customer profile, an ultimate way of data-driven marketing.

Big data is everywhere: deploy it strategically for your organization

Big data is everywhere. Consider vaccine production, here there are more than 200 variables that affect the outcome! As a result, yields vary greatly from batch to batch. Pharmaceutical companies apply big data analytics to identify which combinations of variables have a major impact on the final outcome. By controlling the nine most important variables, this pharma was able to increase overall yield by more than 50%.

The commercial possibilities of big data are also endless. Take email marketing, where personalization is one of the main challenges for marketers to be relevant. With only the data from e-mail marketing campaigns, there is limited way to find out what information the customer would like to see. In a data management platform (DMP), you combine different streams of information together, generating new insights. Purchase history, website visits and customer profiles, for example, make it possible to personalize e-mails even better.

Those who deploy big data strategically in the organization can market more effectively with more efficient operations. The success of companies in the future will therefore depend to an increasing extent on how you know how to use your data.

Starting with big data: think big, start small

Obscure information management, little insight into data flows and lack of (clear) guidelines. Recognizable? Unfortunately for many organizations it is ... take, for example, the handling of complaints: via which channels do complaints come in? Is there a process for this? What is done with this information? To what extent is this data structured and used for improvement? Is it clear who is responsible for this? A disorganized complaint handling has repercussions on customer satisfaction and the image of the organization.

And that while you can make a big difference with a few simple changes, for both customer and organization. Because if you see complaints as an opportunity and use data to improve service, you will succeed in (re)engaging customers. This need not be complex at all, it just requires well-organized business processes and good use of data.

How? By thinking big, but starting small. Think big when it comes to the challenge you want to solve. For example, improve customer satisfaction. And start small by identifying what data sets you have in house to take a step in the right direction. For example, consider your existing data sources, such as the CRM system, ERP system, accounting software or your marketing suite. Then map out what data sets are stored in these sources. Don't rule out less obvious applications a priori; for example, the trip tracking system may be able to provide a better picture of the company visits made by account managers than the CRM. In addition to internal data sources, there is also a lot of data outside your organization that can be requested for free from public agencies or purchased paid through specialized companies.

Trash in, trash out: how do you determine data quality?

Before you get started with data, it is important to determine its quality. "Trash in, trash out," is a well-known saying for a reason. That's why you want to make sure your data is relevant and current. A proven methodology to determine and improve quality is the 5 Rs: volume, variety, velocity, veracity and value.

1. Volume: How much information do you have? In other words, how big is your big data? And to what extent does your organization (still) have sufficient computing power to analyze all this information?
2. Variety: What types of data do you have at your disposal? To what extent is this data structured, semi-structured or unstructured? An example of unstructured data is individual photos. And with semi-structured photographs you can think of data with limited metadata, for example video images with location indication, but without description of the subject. Finally, you have structured data; this data is clearly organized in a clear format. And even structured data is not always compatible in practice: if one department prioritizes with numbers (from 1 to 5), and the other department uses high, medium and low, then linking data is still a problem. Variety deals with the complexity of combining and analyzing data from different sources.
3. Velocity: How fast is the data generated, how changeable is the data ánd how fast can it be analyzed? For some companies, velocity is an important factor, for example if you want to control a production process in a factory in real time. In other cases the velocity is less important, for example when a monthly update is sufficient.
4. Veracity: How reliable is the data (quality)? The less the quality, the less reliable in general the analysis. This is therefore also seen as one of the most important metrics.
5. Value: Value is about the value you can extract from the data. Is the (time) investment of the analysis in proportion to the value it delivers? Do the outcomes help you actually make better decisions?

Big data is the future

Every company has data, which in itself is nothing new. What is new is the growing ability to mine this data. Technology is making data accessible to an increasingly wide range of people, even if you are not a born data analyst. This makes having sufficient data and the right tools important for the (commercial) success of an organization.

In a customer data platform (CDP) like HubSpot, you are able to store data in a structured way, with the right metadata. This creates a good basis for combining data from marketing and sales with information from other sources.