Understanding Business Intelligence and Data Reporting

Frequency

Data Reporting usually occurs on regularly recurring time intervals; for example – weekly, monthly, or quarterly. In fact, these are the intervals that are built into Megalytic’s reporting dashboards. For those working within agencies, reporting frequency and intervals are likely to align with billing cycles. Business Intelligence (BI) activities, however, tend to be more ad-hoc and on-demand. Analysts are more likely to leverage BI when there is a very specific question to answer or a hypothesis to test. These occasions are not regular and predictable like a Data Reporting cycle.

Sources

Data Reporting within digital marketing tends to be broken out along standard, pre-defined, channels such as paid search, social media, organic search, email, etc. By contrast, Business Intelligence often breaks down data in non-standard ways to reveal insights that may not be as obvious. Analysts who are performing data mining and trying to find new, unexpected relationships within a data set, may find that non-standard dimensions are needed to express their insights.

Formatting and Presentation

Data Reporting tends to be very standardized, the reports can easily be compared period to period because the same channels are being used, and the same metrics and/or KPIs are being reported. Stakeholders who generate and review these reports can expect a level of consistency from report to report, right down to where to look within a report for a specific data point or reporting focus. This standardization can breed familiarity with stakeholders.

On the other hand, Business Intelligence reports don't have the same level of standardization, as the questions being asked or data sets being explored are different in each case. The BI queries used during one quarter could be very different from the next quarter’s queries, because the nature of the questions being asked and research required will vary.

Core Questions Answered

This might be the main difference between Business Intelligence and Data Reporting, and the one that determines all of the other differences between the two.

Fundamentally, Data Reporting looks back in time and answers “What Happened?”. Regular Data Reporting often includes an analysis that can drive meaningful change that impacts digital advertising efforts. But, for most organizations, Data Reporting is more tied to transparency and accountability than it is to uncovering deep, paradigm-shifting, insights. Some of the limitations of standard Data Reporting, as mentioned before, are that it is often tied to specific 3rd party platforms and traffic channels that have their own particular set of metrics and dimensions which are reported without reference to other data sources. Without referencing data outside a narrow frame of reference, it can be hard to determine why changes are happening.

For example, Data Reporting can tell you that in the last three months the average cost-per-click for an active AdWords campaign for an air conditioner repair business has gone up 40%. That’s a “what happened” data point. What Data Reporting can’t necessarily tell you, however, is why? The “why” question may be better addressed through Business Intelligence, which can marry AdWords level data to other outside data through a common dimension, like time.

Business Intelligence environments might be able to pull in data like average daily temperatures for the regions the campaigns are covering, which might show meaningful trends. For example, heat waves throughout the campaign regions might have created increased demand for AC repair services. This increased demand led to increased AdWords bid rates from local competitors, which led to increased cost-per-click figures.

Moreover, Business Intelligence tools might also be able to demonstrate meaningful correlations that could be used predictively to change course in AdWords campaigns. In this case, BI might suggest that for every one degree centigrade the daily temperature is above historical averages for that time, the bid rates should move up 10%. By getting in front of emerging trends, the bid rates could be adjusted ahead of time to help secure Impressions and Clicks before the competition drives rates out-of-reach.

This can help frontload leads before the heat wave peaks and cost-per-click jumps up. This semi-real-time, weather-based, kind of campaign adjustment is becoming more common within organizations that are leveraging robust Business Intelligence tools.

Investments and Resources Required

Data Reporting is going to be less resource intensive than Business Intelligence. This applies to both the tools and software required, as well as the experience and savviness of analysts who have been tasked with each. Data Reporting might just require a single digital marketing platform and a channel specific specialist to report on the campaigns. This is fairly common within paid search, for example, where the analysts might only be expected to report on AdWords campaigns.

Business Intelligence, however, requires far more robust data management, that offers organizations the ability to draw from multiple, disparate, data sources that are not always designed to “talk” to one another. Experience in cleaning and tweaking different data sets so that they can be used together is often a prerequisite to even leveraging Business Intelligence applications. As such, BI specialists are more likely to have experience and training with more formal aspects of data science. Although they may not have the same experience in any one given marketing platform or channel, they will typically have a broader and deeper skill set in analysis and problem solving than a dedicated channel specialist.

Conclusion

There are some key differences between Business Intelligence and Data Reporting activities. Any organization that is leveraging BI to drive strategy is most likely going to have Data Reporting as an ongoing business function as well. While each of these types of analysis yield different insights that can be used in different ways, it is when an organization can achieve a clear understanding of how these two forms of analysis differ that they can be effectively used together.