How much time are you spending manually forcing your data into some useable format to make critical business decisions?

THERE IS A BETTER WAY, AND YES, IT MAY INVOLVE JUMPING INTO A LAKE!

Before explaining what I mean, let’s explore the tedious process most companies will go through to get these insights. It typically goes something like this . . .

• You have an idea that will change everything, but need data to back up the theory.

• You spend some time thinking about what data sources and systems you need to access to get the relevant data (ERP? CRM? Inventory? Marketing’s massive spreadsheet?).

• Next is calls and emails into the owners of each of these systems to see if the data is available.

• They think, look at the ground, shift awkwardly and say “yes” but it will take some time.

• Two to three weeks later you have your data.

• Now the fun part . . . getting all into a single spreadsheet.

• After hours of merges, if/then formulas and pivot tables, you put your head in your hands and cry a little, give up, and dream of a day when you can hire someone to do this!

THE SOLUTION

Ok - enough already, you get it - let’s get to the better way! More and more companies are moving to a central data repository to solve this problem, often referred to a data lake or data warehouse as the solution (there are clear differences between these two that we’ll save for another time, but if interested you can read about them here.) The general idea is that all of your data sources integrate with this cloud based repository and since all of your data is in the same place, building the reports and dashboards you crave is a snap!

For many of you this isn’t an earth shattering concept. You've probably heard it before, and wrote it off because “it’s only for big companies”. Or you explored it, but felt your systems were too outdated to create the necessary integrations.

The reality is that this nirvana is achievable and affordable for businesses of all sizes. Any of the challenges mentioned above can be overcome with the right set of eyes looking at the problem. The Indy Res team has done it before can help talk through what the process would look like and associated costs.

PRIMARY STEPS WE TAKE:

PROJECT
DISCOVERY

• Outline desired use cases for your data project

• Determine data sources

• Determine the right tool for the job (we’ve worked with AWS, Elastic, Snowflake and Microsoft Azure)

CONNECT DATA SOURCES

• Leverage available (APIs)

• Build integrations where API do not exist

REPORTS AND DASHBOARDS

(More on this in future posts)