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The shift to event-level data

Sachin Bhatia
Sachin BhatiaAP Data Center Group Marketing Director
I love the landscape of customer advocacy. I am passionate to understand how & why customer segments behave differently, what motivates them and how brand should talk to them as a problem solver rather than a solution seller. Hi, I am Sachin Bhatia and I am Head Marketing for Lenovo’s Data Center Group in AP region. I truly believe technology can unleash customer’s business potential which enables creativity, open new door of opportunity and above all, solve the problem seamlessly.

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There is no arguing with the fact that data is the new buzzword in business—no matter the industry. Data enables businesses to identify what their customers like and dislike, what makes their employees happy and unhappy, what aspects of products or services could be improved, what consumer niche they are missing out on, and so much more. But, all too often, data is just generalised into the one term. This can be a big mistake because there are different types of data and they can be utilised in many different ways. Event-level data has recently proven itself to be a very valuable kind of data that all of the top organisations are keeping an eye on.

What Is Event-Level Data

Event-level data is one of the richer forms of data. Many attributes can be gleaned from it that help to explain the state of something or someone at a specific time. Event-level data is all about actions. It is what users, customers, and consumers do and when they do it. It includes everything from the pages they visit on a website to the history of purchases they have made. For example, a business could use event data to see how impactful a recent promotion was. They could identify how many first-time customers were enticed by it, how much more it encouraged customers to buy, whether it influenced former customers to buy new products, and so much more.

For the average business, over a longer period of collecting event data (customer actions) like clicking on an ad, walking into a retail location, and updating a profile, can be both tracked and then analysed. When all of these actions, or events, are strung together, they help companies to have a clear picture of their customer and how to effectively market to them.

But event-level data is nothing new. It has been around for years. The problem with it, however, was that it was only available to the biggest corporations because it was too expensive to collect, store, and analyse. Because of the uniqueness of event data, it requires not only a specialised approach but also specialised access patterns and functional architecture. When data analysis first became a business strategy, event data required the most resources. Large teams of skilled data engineers and data scientists were required to process and analyse event data. Now, one person can capture all of the data, run queries, and access results in real time—all it takes is the right tool. Additionally, cloud database price points are significantly lower than incumbent, on-premise databases, making data storage a viable option for even small businesses and entrepreneurs.

The Shift

For years, data warehousing and analytics have used the Extract, Transform, Load (ETL) method. It was the most cost and technologically effective way of dealing with the rigidity of traditional data centres. Recently, however, there has been a shift to Extract, Load, Transform (ELT). The Cloud and the cheaper databases that came along with it has made this newer method more affordable and viable.

But to understand what this shift means and why it has happened, it is best to first understand what these methods are. ETL means Extract, Transform, and Load. Data is first extracted from its sources. It is then transformed to fit the necessary operational needs. Finally, the data is loaded into the database or warehouse. ELT, on the other hand, stands for Extract, Load, and Transform. Data is first extracted from the source, just like the other method. It is then loaded into its database. It is finally transformed to fit the necessary operational needs and then once again loaded into the target database.

This switching of the loading and transforming stages, from ETL to ELT, provides several significant benefits. The first major one is that with ELT, because all of the raw data has been loaded, users can test, run new transformations, and enhance queries directly on the raw data. With ETL, users had to wait for a single transformation to be completed and loaded before they could run a new one—a much more time-consuming way to query. With ELT no waiting time is required. Other benefits of ELT include the fact that data size does not slow down the process, the data is always available, it enables the option of data lake use, and finally, because of its scalability, Software as a Service (SaaS) solutions make it accessible to businesses of any size.

More can be achieved with ELT and because of that, many are switching over. And this shift is being made simple through the tools that are now available. These tools include software like Segment, Amazon Kinesis, Alooma, Snowplow, and

How Is Event-Level Data Being Used In Asia

Event-level data, due to its newly increased accessibility, has begun to take off in Asia. Globally, the software that helps to aggregate and manage the event data is a U.S. $21 billion market. And over the next decade, it is expected to expand by roughly 12%. Most of this growth will come from Asia, which is tied to its increasing demand for venue and event marketing management solutions. In fact, by 2022, Asia-Pacific alone will likely have an event data software market of more than U.S. $1 billion.

One of the best examples of this likely Asian expansion is several Asian travel startup companies’ use of event-level data. They are doing everything from providing real-time feeds for traditional transportation suppliers to providing hotels with a tool that will allow them to see how concerts and sporting events will affect their business in real-time.

Another example of how valuable event-level data is becoming in Asia can be seen through how highly it is valued by marketing departments in every industry. And this value is simply driven by the fact that, in Asia, more than a quarter of most marketing budgets are spent on events. For this type of spending to be validated, a Return on Investment (ROI) needs to be proven, and event-level data is how that is accomplished.

What Are The Benefits Of Raw, Event Data

1. Higher Fidelity: Event-level data is, as previously stated, one of the richer types of data. It can provide a fuller picture of both consumers and their habits. And, because of this, the data tends to be more trustworthy. It gives users objective facts that help with reliable business strategy formulation. Additionally, when businesses own their own event-level data, there is a higher level of both granularity and clarity that they can use with more confidence.

2. More Flexibility: Event-level data is simply more flexible. The raw data that is captured enables users to perform just about any type of analytics on it, meaning more and better insights for businesses.

3. Foundation For New Products: Event-level data allows companies to serve their customers better. They can use the data to build apps that can effectively engage users in real-time. Apps that can ask users for more information or ones that can suggest actions or products—and not in a way that the user finds annoying, but helpful. And it goes beyond just creating a more engaging app. Businesses can use event-level data to see hidden patterns and then develop new and better experiences and products, ones that will help to generate more revenue.

Event-level data is more than just any other data. It has the potential to provide the type of insights that make businesses stand out in all the best ways. And due to the tools that are now available, it can be scaled to fit any business size, allowing small businesses to compete with the giants of any industry.

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