Just Give Me The Data

Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.

Today’s column is written by Steve Marosi, director, client advisory, at 84.51°.

Why is it that many analysts – who have so much data, yet so little time – turn up their noses at reporting systems? Why do they lose patience and reach for the export button, doing their most interesting work in Excel, Tableau or other roll-your-own tools? They’re so quick to tell data/insight providers, “I don’t want your dashboards and reports – just give me your data!” Why?

A data analyst recently told me that she exports data from upward of 70 reports to prepare a monthly dashboard for her department. Extreme? Certainly. But not unusual.

This quest for raw data is a message heard repeatedly from consumer goods companies. I imagine it’s not much different in other industries. Yet, what is it behind this self-inflicted pursuit of raw data with all its messiness, hours of toil to clean and harmonize and high risk of introducing errors?

The answer, in one word, is personalization.

Here’s my working definition for personalization in the context of data analysis: the freedom to shape an analysis, with minimal restraints, to address a very specific business problem or question.

Data analysts prize personalization so much that they’re willing to endure endless complexity, tedium and manual effort because it lets them find and communicate insights tailored to the problem at hand. For many analysts, standard reports don’t deliver insights; they serve as data pumps – valuable only for the content they deliver through the export button.

Should you care?

If you’re a vendor of analytic insights, each export implies that your system has failed the user in some way – either to deliver the important insight or to enable the necessary formatting and context.

If you’re a decision-maker, your analysts are not only wasting precious time, they’re multiplying the chance for blunders, which can lead to wrong conclusions. Every analyst I know spends far too much time pulling and polishing data and too little time analyzing it for insights.

But is exporting data always a mistake? No. There are two good reasons for it. First, no system can anticipate every possible path or analytic that a user will want to investigate. And second, to find certain insights, an analyst may need to combine data from one system with other sources.

These reasons both support what I call “exploratory analysis.” In nearly all cases, exploratory questions emerge from answers to more common and less demanding questions. An analytic reporting system for a given domain should anticipate and deliver the common insights.

But no system can anticipate when, how or in what order an analyst will proceed through an investigation. The artful science of data analysis defies repeatable processes. Human intuition and imagination seep into every zig-zagging step, meandering along a path of questions that often lead far from where the analysis started. Wandering one’s own path is vital if an analyst is to find the essential insight. Equally important is communication, the ability to tailor insights to the decision-making audience in a way that leads to understanding and action.

I see three objectives for personalization.

Freedom To Wander One’s Own Path

Pre-built analyses deliver far more speed and precision versus starting with raw data. An analyst, progressing through common, early-stage questions, should be able to rapidly explore a variety of hypotheses, leading to promising new questions while dodging blind alleys. This leaves more time for custom number-crunching in the later stages of an investigation – where the analyst really earns his or her paycheck by exploring uniquely relevant and valuable questions.

Pre-built analyses will still require a bit of configuration, such as user-defined thresholds, for example, but, designed well, they could serve as a happy medium between rigid reports and unfettered raw data access.

Unobstructed Access To Raw Data

Why allow raw data access at all? Less-skilled analysts might combine data tables and columns that result in absurdities, or they might misinterpret a measure because they didn’t double-check the definition.

But these risks are no worse than the situation today where an analyst exports data from a wide variety of reports and data sources, combining them manually in a spreadsheet however they wish.

Flexibility To Shape Insights Into A Story

Every analysis spews out tables, charts and other artifacts that require pruning before they’re ready to share with decision-makers. Giving users the ability to edit and rearrange their story within the system itself avoids the need for data dumps and screen captures merely to format and annotate.

Striking A Balance

The future of data analysis must somehow enter the fluid state between solid (standard reports, dashboards and landing pages) and vapor (limitless raw data access). The opportunity is huge to strike a balance between these extremes, increasing the speed and impact of data analysis in every industry.

Follow 84.51° (@8451group) and AdExchanger (@adexchanger) on Twitter.

Enjoying this content?

Sign up to be an AdExchanger Member today and get unlimited access to articles like this, plus proprietary data and research, conference discounts, on-demand access to event content, and more!

Join Today!


  1. Steve,

    You’ve raised some excellent points!

    As someone who oversees a large number of data analysts and other data-driven professionals (along with rolling my own data insights), I can validate first-hand that raw data is the new oil. In my view, the top reason for the need for raw data is the ability to understand a real-world business situation in more detail and then answer different types of questions than those answerable via canned reporting.

    The runaway success of Tableau Software is rooted in their feeling that they serve the data analyst — not the IT manager — by delivering “data to the people.” I think the real future is in providing a rich set of robust but semi-flexible “canned” reporting to the business while working with raw data on an ever-increasing basis.

    If anything, I predict that the demand for raw data will continue to grow unabated. The use and provision of raw data should be part and parcel of every product from inception, whether that product is related to ad-tech or not.

  2. Having been in performance marketing since 1991, I can’t think of a time when the pre-canned reports provided by an analytic vendor ever met my needs. Partial fits? Sure. But there are two things I’ve seen in every organization I’ve worked in or consulted for:

    1. Idiosyncratic analytical needs required by the company (whether they really helped drive the business was another story)
    2. Ever-changing and unique requests from the executives that could only be answered by somebody that sat in the Monday morning review and sat through the five weekly analysis fire drills.

    In case one, going back to the supplier often comes back with an $X change order which is far more expensive than ginning up an Excel macro. In case two, you’d spend a day trying to just describe the problem to a solution vendor or external analyst and the resultant output usually wouldn’t fit the need.

    I’d love to have my analyst time spent doing analysis instead of exporting and munging through data in Excel, don’t get me wrong. But I haven’t seen that reality change much in 27 years.

  3. I don’t think number one in the list is personalization, it’s integrity.

    If I present insights from data then I want to have sliced and diced it: I don’t want to trust that someone else has aggregated it correctly for me. And further, it’s in the data preparation that you learn it’s idiosyncrasies; when you prepare data you get a feel for the story it’s telling you. This lets you respond to questioning, challenges, and gives your analysis believability.