Jun 12, 2014

The Efficient Analyst (part three): No more waiting


Nothing kills analyst productivity like the waiting game. In a world with big demands and tight deadlines, hitting roadblocks to progress can result in huge frustrations and last-minute scrambling. Where does this waiting time come from? Quite simply, analysts must wait for data if they can't get it on their own when they need it. In part three of this series, we'll review the causes of analyst wait time and offer suggestions on how to fix it.

The drivers of wait time

Analyst wait time comes from two sources: humans and technology. On the human side, data bureaucracies or process bottlenecks can make analysts dependent on the availability of other data-capable colleagues, which can burden specific individuals and create internal strife. On the technology side, bottlenecks are caused by a lack of accessibility, such as permission restrictions. hard-to-use interfaces, or complex ETL processes that do not occur frequently enough to meet the needs of data users.

The human side

Process bottlenecks arise out of labor specialization. Perhaps there is a member of your team (let's call him or her "Data Person") who has been entrusted with managing all or some of your organization's data. Data Person might have the best data skills, or the volume of data might seem to require a point person to maintain it.  

It's important to note that Data Person is not JUST a data geek, IT professional or programmer. They are THE person (or persons) on whom the organization depends to get, use, filter or generally utilize data. There are some obvious problems with this. First, if Data Person is sick, on vacation or leaves the company, analysts don't have access to the data they need (or will waste time trying to figure it out on their own). Second, if Data Person is swamped by requests and new data updates, bottlenecks can cause analyst work to be delayed or create stress among the team (including and especially Data Person).

Good data persons try to document processes so that their absence or busyness does not slow things down. While documentation is nice, it does not solve the greater issue: the skill gap between Data Person and the average data user. 

The technology side

Challenges arise when technology creates a barrier to data access. This can be by design (access is restricted on purpose) or due to structural complexity (getting the data is simply too hard for the average data user). Either way, lack of access almost guarantees data bottlenecks. Even in cases where there are no permission restrictions on data access, hard-to-use data structures still mean that Data Person must query or extract the data for the user.

Reduce waiting time by closing the gap

Closing the gap that causes wait time must be approached from both angles. Technology must be used to create a system where data is easy to access and meets ever-changing analyst needs. This can be done creating simpler interfaces, less complex data structures or by empowering analysts to execute ETL jobs so that they are in greater control of their data they need in real time.

On the other side, Data users must be well trained and comfortable using data so that they are empowered to get what they need when they need it. Data training usually requires substantial effort to enhance the capabilities of the entire team, and it can take months to get everyone up to speed. This is not a quick fix - it's an investment in the future productivity of your organization.

But what about Data Person?

We are not advocating for an elimination of the Data Person role but rather a re-allocation of that persons skills. Rather than positioning him or her as reactive to the individual data needs of the organization, Data Person must be proactive in implementing easy-to-use technical solutions while training analysts to use it. Oh, and do this will also doing their regular jobs during the transition. Easy? Not so much. But give us a shout - we might be able to help.

Data persons or data users - do you have any thoughts on this? What's wrong with our approach? Are we being realistic about the capabilities of organizations to close "the waiting gap"?

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