Let’s talk about an inconvenient truth: For the typical business, data reporting has a tendency to fall short of producing the desired outcomes. Despite the significant resources that organizations often invest in producing data reports – and in the data collection, governance, and analytics processes that happen prior to reporting – the people who actually need to make decisions based on data don’t always find reports to be all that helpful.
That’s not because the concept of data reporting is flawed. It’s because too often, data reports don’t provide stakeholders with the information they need to make effective decisions, especially when data from multiple systems or business functions is required to make those decisions.
Fortunately, this is a problem that businesses can solve, although doing so requires more than simply reformatting reports or merging information from multiple data platforms in a simplistic way. Instead, as I explain below, improving the value of reports necessitates an investment in the concept of data activation – a practice that helps make data as actionable as possible, regardless of where it originates or who needs to interact with it.
Why Data Reporting May Fall Short
A typical data analytics workflow looks like this: First, businesses collect data. Next, they perform data quality and governance processes to ensure that the data is properly formatted and structured for their goals. They then analyze the data to detect relevant patterns or anomalies. Finally, they summarize their findings in reports, whose purpose is (in theory) to help stakeholders within the business make informed decisions based on data.
The problem, however, is that it’s one thing to generate a data report, but it can be quite another to generate a report that’s actually useful to whoever is making decisions.
Again, this isn’t simply an issue with the way reports are formatted or how they present information to users. The problem runs deeper. It stems from the fact that in many cases, reports are based on siloed data, which leads to siloed decision-making.
For example, imagine that you are an HR leader making decisions about how many sales staff you’ll need to sustain projected sales activity over the coming year. This decision requires insight from two distinct types of data: sales data (which details anticipated sales activity) and HR data (which tells you how many sales your existing staff can support).
At most companies, this data would be stored in two separate systems, such as Salesforce for sales data and Workday for HR data. Each system can generate reports on the data stored in it. But if you want to bring that data together to produce insights that span multiple business functions, the systems won’t be very helpful because they don’t have access to each other’s data by default.
Therein lies the root of the problem about data reporting: Reports lack the broad insight necessary to drive decision-making that requires stakeholders to think beyond individual business function. If you only care about sales or only about HR, for example, conventional reporting might work well enough. That changes if you need to analyze sales and HR operations simultaneously.
From Data Reporting to Data Activation
To step beyond these limitations, businesses must extend their data reporting strategy so that it enables data activation.
Data activation means unlocking value from data. It’s different from data reporting because data activation enables impactful decision-making, whereas reporting simply amounts to summarizing data analytics findings.
To achieve data activation, organizations must ensure that their data analytics and reporting strategy enables two key outcomes:
- Delivering data insights in the right form, based on the right data: Reports should be tailored to who is viewing them and which decisions they need to make. A report that draws on data from both sales and HR systems might identify the same core trends, for example, but it should highlight different findings for sales and HR audiences. To do this, you need to customize not just how reports themselves are structured, but also how the data filtering and analytics that drive the reports happen. Even if the underlying data set is the same, the way you explore and analyze the data may need to vary depending on your audience and use cases.
- Delivering insights in the right place: Decision-makers often lack the time or skills necessary to go searching for additional insights that are missing from the reports delivered to them. For that reason, it’s critical to make reports easy to locate and easy to use. You shouldn’t expect an HR executive to go looking in Salesforce to find insights that are not available through Workday, for example. You need to make the insights available in Workday or other systems the HR exec already uses.
In short, data reporting can drive data activation. But in many cases, the default tools and processes that businesses use for generating reports aren’t enough to activate data. Organizations need to tailor the reports themselves, as well as the data analytics processes that lead to the reports, to meet data activation goals.
The Role of Reverse ETL
How do you actually do that? When you have data that is siloed across multiple systems, how do you analyze and report on it for multiple audiences, while ensuring that you customize those processes for each group and/or use case you need to support? And how do you do all of this without a tremendous amount of manual effort?
An efficient solution is a technique known as reverse ETL. Reverse ETL (ETL stands for extract-transform-load) is the process of moving data that you’ve already processed and pushed out to a data lake or warehouse back into the business application where the data originated.
For example, using reverse ETL, you could pull from your data lake two sets of information – one that originated from Salesforce and another from Workday – and ingest them back into Workday. You can then report on this data through Workday.
The outcome would be a report that draws on cross-functional data (because you’re analyzing data from both a sales system and an HR system), and that is accessible in the data platform (Workday) that your HR stakeholders use. If HR is your target audience and making decisions about both sales and HR is your use case, you’ve activated your data by delivering insights derived from the right data and delivered to the right place.
Getting More Value from Data with Data Activation
The data activation example I’ve described above involving insights drawn from both sales and HR information is just one of the many use cases that data activation helps support. Virtually any complex business decision – such as analyzing customer behavior to drive marketing campaigns, or setting price points based on customer buying trends as well as product cost data, to name just a couple of other examples – requires a cross-functional, tailored approach to interpreting data.
Conventional, siloed data reporting systems don’t enable that approach, but a strategy centered on data activation does.