This cartoon hits close to home. Most of us that have been in the data space have received some sort of comment like, “You just build reports, right?”
This is funny to data folks as it is like saying the interior decorator built your house. With home building, a decorator is very important. They take the space that is available, optimize it, and, hopefully, help you enjoy your home. That said, months of work need to happen before that action can take place.
Data work is very similar.
Before anyone starts building a report, certain questions are going to be considered. A sample:
– What is the business objective you are trying to solve or understand?
– What data is available? Is it formatted to meet your needs?
– Is available data accessible to do the reporting and analysis you need?
– Do you trust the information available?
When building a house, an architect or contractor will create, and review blueprints and plans for the desired home. With data/reporting/analytics projects, a data architect or engineer will often work with data or business analysts to review- and in many cases design- data flows, data models, and architecture to understand how business needs can be met . Additionally, existing data sources must be identified along with plans to address data quality and remediate issues as they arise.
Once blueprints and plans are complete and approved on a house, a builder will start construction. They will source and acquire tools and materials and begin work. Building a reporting and analytics platform follows a similar path. There are whole books on methodologies, but rather than focus on how projects are constructed, this post will focus on what types of activities need to be done.
With data projects typically someone with a title like data engineer performs construction activities. These range from standing up a database or a big data platform (similar to a home foundation), to ETL (Extracting, Transforming and Load, similar to framing and building of the house) to more basic data curation (Obtaining materials), data clean-up (addressing material defects) or various other items. Completing a project that starts from scratch could take anywhere from days to months depending on the complexity of the solution. When someone says, “you just build reports”, this type of heavy lifting is often forgotten.
In homes, the interior decorator will often choose and acquire aesthetics during construction. For example, paint colors are decided, furniture purchased or narrowed down, lighting decisions are made, appliances are decided and purchased, and art and visual decisions are considered or made.
With Analytics and Data projects, the data engineer, data scientist, or data visualization professional has similar design tasks such as defining the reporting/analysis tool, wireframing, refining the business problem definition or selecting Key Performance Indicators. Sometimes prototyping is possible, helping make sure the undertaken approaches meet the business need.
Once the home is built, the interior designer implements the chosen design. Paint colors are finalized, paint is applied, appliances are installed, flooring is installed, furni
ture is purchased and installed, light fixtures are installed and finishing touches are implemented.
This is when “you just build your reports” applies. There is an infrastructure that exists that supports business needs, the necessary data is available, and the details can be applied. Data Visualization or analytics tools such as Power BI, Tableau or Qlik and others can be leveraged to provide the insight that has been determined.
That said, just like with a home, there is always something that needs to do be done (fix a leak, replace a toilet, replace your roof, build a deck, etc.).
Data is no different. Once data is being used, it will drive a thirst for more. Like building a home, the work is usually much smaller than the initial build, but it takes work to evolve.
ImagineX is experienced in all phases of data development. We can help you understand where your challenges no matter where you are in your data evolution.
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