8-MINUTE READ · By Mina Nacheva
Marketing data is evolving. It is moving faster than most marketing professionals can even anticipate, so it’s crucial to understand that the processes around it are changing, too.
Quite literally, you can say that data integration is about integrating marketing data into your business planning, operations and decision-making. And you’d be right. But what does that actually entail?
Data integration is the result of data being generated by various platforms and sources, collected, cleaned, sorted through and eventually brought to business stakeholders in a shape and form that they can directly use to make decisions. Simple, right? Well, not necessarily.
Nowadays, organizations are active on so many platforms – anything from Facebook and LinkedIn, to Microsoft Advertising, Google Analytics and email marketing solutions like Mailchimp, that the amount of data that they generate is nothing short of overwhelming.
Companies may have plenty of data but it is often messy, unstructured and out of place. So what if you could turn it all around? Data carries insights with itself and when integrated the right way, it can bring plenty of benefits to the business. For one, data integration is about…
…getting the data you want, where you want it!
Marketing data comes in large amounts and at an ever-increasing speed, yet it never comes ready to use. It needs to be handled in a way that it is harmonized, stripped of various formats, and complete. Missing data can distort the insights that are being derived from it and can have a negative influence on decision-making. In order to avoid that, make sure that your data is clean and consistent, and that it is all brought into one and the same location.
With Supermetrics you can move all your marketing data straight into Google BigQuery
Getting everybody on the same page
Having one location for all harmonized data is another important ingredient in the integration process, as it makes it easy for all company stakeholders to be able to find it, read it and interpret it. The key here is to eliminate silos, which – whether we like it or not – are quite likely to exist, and to centralize the flow of information. That way, this central pool of data can be accessed and used by everyone and thus enriched every time someone connects two or more data points.
So, while you may be looking at the marketing and advertising aspects of last week’s sales, your colleagues in finance might be interested in just how those sales contributed to the company’s bottom line. You will be using the same datasets but in different ways and for different purposes.
Getting the insights your business deserves
Good-quality data is at the heart of making the right business decisions. Data that is consistent and relevant gives decision-makers that much more insight into the matters at hand, and allows to draw conclusions based on solid facts instead of rough estimates. Not to mention the benefits it can bring to predictive analytics and anticipating market trends. In order to do so, however, it is also important to have data well visualized and shared across the company, which is where tools like Data Studio can play a key role.
There is no doubt that data integration can improve the consistency of your data, the collaboration between teams, and the quality of your business decisions. However, there remains a question:
When is a company really ready for data integration?
While this readiness has a lot to do with technology, it is also about company mindset. The fact is that organizations that handle growing amounts of data every day and use it actively to make business decisions are likely to embrace data integration sooner rather than later. That would also include moving away from manual coding and processing of the data, to automation.
While automating data may sound like a logical and necessary step for such companies, it is also a rather complex one. In fact, in order for an organization to derive real knowledge and value from their data, they need to collect it in near-real time – or pretty much as soon as it is generated. That also means collecting the data close to its original source so it can be processed in its rawest form possible.
Speaking of collecting data, we cannot forget mentioning an abbreviation that is quite vital to this entire process: ETL.
ETL as part of the data integration process
Short for Extract, Transform and Load, ETL is a process that enables marketers to collect data, clean and harmonize it, and load it into a database where it can be found ready for analysis. As such, it is often confused to be the same as data integration, yet ETL alone is often no longer enough to handle the growing amounts of data out there.
Data integration includes another process, too, abbreviated to ELT, or Extract, Load and Transform. It is a variation of ETL, but a variation that brings significant difference to the process of handling data. While automated processes make it easier on marketing professionals to manage all the data coming their way, they also tend to take away the human element of passing judgment on that data.
ELT, on the other hand, allows marketers to take a closer look at their data after it’s been loaded into the database, but before it’s been transformed. This adds a human touch to data integration and gives marketers a chance to assess which parts of their data are most relevant and valuable to their analysis.
The challenges of data integration
Finding the right approach to storing your data
While the goal of data integration is to make it easy for people to analyse it and make decisions on, it needs to be clear to what extent this data is going to be used in the first place. Based on that, a company’s data professionals can decide how and where to store the data.
A data lake or a data warehouse?
Data lakes and data warehouses are both feasible options for storing marketing data, depending on what that data is later going to be processed and used for. Data lakes are pools of raw and unstructured data, whose purpose is not fully defined yet. While they offer a lot of possibilities to look at that data, draw connections and identify patterns, data lakes are rather complex for the average data user to work with. They are, thus, mostly in the hands of experienced data professionals.
Data warehouses, on the other hand, are the home of processed and structured data that is currently being used by sales and business professionals to make decisions. They are a lot more accessible and do not require advanced data knowledge to handle them.
Since there is no single approach to data integration, it is crucial for companies to analyse their own data needs and make choices based on them. While data lakes might be a good opportunity for companies that want to monitor trends and predict market developments, data warehouses might be the right approach for business that want to focus on making fast decision here and now.
If we take transportation as an example, a company in that industry would be interested in responding to current developments, but it would be even more interested in monitoring market, customer and seasonal trends. In that case, a data warehouse might already be too structured, while a data lake, where the data has not yet been processed, might provide more opportunities to identify patterns and draw connections.
Achieving the best data quality possible
While there may not be a one-size-fits-all solution to storing your marketing data, there is no discussion on the fact that its quality can only be the best possible. It is crucial for your data to be up-to-date, clean and complete.
As it is becoming more and more important for businesses to act on timely and relevant data, it is up to marketing and data professionals to make that possible. Making sure that data is collected in near-real time and transformed as quickly as possible enables decision-makers to stay on top – and sometimes even ahead – of trends.
Each of the platforms companies use have their own ways of collecting and presenting the data generated. Facebook will show the data collected from the latest Facebook campaign. LinkedIn will do the same. While marketing and data professionals can go through those individual datasets one by one, they will not be able to see the bigger picture – and definitely not in near-real time. This is why an overarching data pipeline tool becomes so important nowadays.
Integrating traditional on-premise and cloud data
Another main challenge is finding an efficient and feasible way to integrate on-premise and cloud data. It is likely that many marketing professionals are looking to transition their data processes to the cloud, or at least combine existing in-house solutions with it.
Cloud solutions are becoming more sought-after due to their lower cost of entry and high possibilities to scale. They also meet high security standards and enable users to work flexibly. In-house data warehouses, on the other hand, have a physical footprint and require companies to have their own workforce dedicated to managing all incoming data. While that can be more costly, it allows companies to get access to their data sooner, instead of waiting for it to bounce through a number of servers online.
Opting for either one of the solutions is up to businesses themselves, yet it is important to remember that the cloud is evolving by the day, providing professionals with more and more opportunities.
Finding the right data talent
In order to have data integration work like a well-oiled machine, businesses need to make sure they have the best talent working for them. While marketing professionals are increasingly learning about data processes, in-depth knowledge of the matter is becoming more and more of a must.
In recent years, there has been talk about the need for companies to hire a Chief Data Officer (CDO) to be the bridge between IT and the rest of the business. While creating such a position would likely depend on the size and data needs of the company itself, a CDO can help take away from the increasing workload of marketers and smoothen out the integration process – from gathering the data to making it ready for analysis.
What to look for in a data pipeline tool?
When looking for the right data pipeline tool for your company, there are several key points that are good to take into account.
Ease of use
The data integration tool that a company chooses should be easy to use and intuitive, as its goal is to essentially save time on work previously performed manually. Part of that is its ability to generate simple and clear visualizations that make the data more tangible and easier to grasp.
Use of the right naming conventions
For marketing and data professionals, naming conventions can really make the difference between finding the data they’re looking easily and quickly, and looking for a needle in a messy data stack. Especially if they are collecting data from various sources throughout various geographies. Something as simple as writing a data format or a country abbreviation wrong can make looking for the right data so much harder.
Ability to connect to multiple platforms and applications
The more a company grows, the more likely it is that it will be leveraging new and different sources of data. In order to keep a single overview of all the data that is generated, a good data pipeline tool should have as many connectors as possible. In other words, it should go way beyond only integrating with Facebook, Twitter or LinkedIn.
An end-to-end data marketing solution
With regard to the point above, the tool a company chooses needs to essentially be an end-to-end solution for their marketing needs. While it needs to connect and harmonize complex data, it needs to also provide appealing possibilities for visualizing and sharing the insights that come out of that data. Integrations with platforms like Data Studio, thus, become key.
Compatibility with the cloud
As mentioned earlier, the cloud is gaining prominence in the marketing data world, which is why the right data integration tool should be able to work flawlessly whether exclusively on the cloud or in combination with an on-premise data warehouse.
At the end of the day, implementing data integration into the workflow of your company is a big and challenging endeavor, but it is one that will eventually save you time, money and effort by eliminating a lot of manual work and (possible) errors along the way. Marketing data holds plenty of insights for marketing and business professionals alike and is the key to setting you – and your business – apart from your competition.
About Mina Nacheva
Mina Nacheva is a Content Marketer and Independent Consultant with a background in media, business, and the marketing data warehouse field.