top of page
  • Writer's pictureJack

Simplify Data-Driven Decisions: A Data Strategy Framework for Your Small Business

If you're in a position where you've got a bunch of systems working for you, but none are talking to each other and you can't quite be data-driven, we're here to help!


We'll go through a data strategy audit and how you can perform one on your own business to come out with a data strategy framework with actionable items.


This is a solution for the situation where it takes you more than 5 minutes to find the data answer you're looking for. If you don't have the folks internally with the necessary skills or you simply don't have the time, this will hopefully clear up the mental mess that is your data situation.


We're going to start with the business focus (business goals) then go deep into the data portion, and finish with taking action!


We, of course, offer this as a service, but our goal is to help the whole small business world be more data-driven, so we're giving you our outline on how to do it here!


TLDR:

To do an internal data strategy audit, start with the business goals you want to hit. Make sure they're measurable and trackable.


When looking through your tech stack, make sure the data fields you need can have at least one common link between them so you can create a single source of truth (probably in the form of a database).


Finally, make sure these data points and KPIs can be displayed in a dashboard that's easy to use, understand, and update.


If you can do that, you're golden!



Data strategy framework in 3 steps



Business Focus

Align your data strategy framework to your business goals


Don't even think about the data part yet. Just think about your business goals - you know them. We start here because all future steps have to be tied back to a business goal. 


Looking at data for the sake of looking at data is useless. Using data to measure and track towards a business goal is highly beneficial. Data strategy supports goals, not the other way around, also.


We could dive into the correct ways to create a business goal, but for simplicity’s sake, let's use a straightforward example of a professional services company growing $1M in revenue next year so we can focus on the data strategy part, not the correct business goal part.


You'll likely have multiple goals, and that's good! You should. Each goal should be trackable and informed by data and they're normally related enough to where one data strategy framework can set the groundwork for all of them.


We'll continue with just one example, but know that in real life, we'll tackle them all.


Measure your business goals


Measuring your business goals


Whatever your goal is, make sure it's measurable. For our example, it's straightforward: an additional $1M in revenue in the following year. For yours, make sure there is a quantifiable piece of it and a time-bound piece of it - we'll need those for the data parts too.


In order to achieve our goal, we'll need some other things to happen:

  • More leads 

  • New clients 

  • Make sure we're growing profitably


In order to keep those measurable, we should set data-driven goals for them too.


When you do this in your business, think through what's needed. To get $1M more dollars, we should know our average client revenue, average conversion rate, target hourly rate, and things like that.


Without going into that process here, let's just set some targets.

  • We need 50 leads, 

  • 10 new clients, and 

  • an average hourly rate of $200


Now we know what we have to measure so we can move to the data part and make some data-driven decisions!




Data focus

Creating a data strategy framework for your small business


Our goal here is to

  • Identify the data sources

  • Identify the specific data fields we'll need

  • Understand how the fields we need are input

  • How the data sources can talk to each other

  • Create some semblance of a single source of truth

  • Automate it as much as possible 


Easy right??



Data inputs: Identify the data sources


Where is the data held? 


Normally this is regular software that you use everyday. For example, Quickbooks for accounting (revenue and profit specifically), Hubspot for a CRM that has lead funnel info, and Clockify for time tracking.


Whatever software you normally use, you don't necessarily have to change anything, just identify it, and we'll make sure we can incorporate it all together. 


Data inputs: Identify the data fields 


When you export the data (or automatically send it to the next source) what are the exact fields you need?


Pay close attention to date fields (ex. Close Date), fields that can correspond to another software (ex. Client name), and the metric you care about (ex. Revenue).


Understand how the fields we need are input


For revenue and financial things, Quickbooks will handle this naturally but you do have to make sure things are input correctly so you can (for example) find the client name in Quickbooks and in your CRM. 


For leads and conversions (new clients in our example), how are those input into your CRM? Are those manual inputs or automated? It doesn't necessarily matter for analysis, but it's important to know so you can easily diagnose issues, problem solve, and create solutions easily.


You want to focus on the manual inputs more than the automated ones. Manual means that there is potential for small things to be off, like an incorrect lead categorization or a slight misspelling of a client name.



Data framework: How can the data sources talk to each other 


“We just want one source of truth” - every person ever. Especially small businesses that think it should be easy enough to have one platform take care of everything. This is a really important part of any small business data strategy.


If you're lucky enough to be in an industry and of a certain size where one platform can take care of everything, that's awesome and that platform should be commended.


For the rest of us, one source of truth is really hard to find. However, one database that holds everything can essentially act the same! This is called a relational database. Remember to make sure there is a date field and a field that can talk to another system. Meaning Client Name in Quickbooks is the same as Company Name in Hubspot, or something like that. If you're able to add dates in there (close date, invoice date, billable hours on a specific day, for example) then you've got something that can work!


Linking these together can be tough, but these days, ChatGPT and others can help you combine them and even write code to combine them for you.


There's your one source of truth! Multiple data sources that can link together and speak the same language.


This is the “some semblance of a single source of truth”. It doesn't have to be in one system, but one database counts!



Data framework: A single source of truth for your data


Spiking this out from above in case you're skimming…


Bottom line here is that we all want one platform to handle everything but it's incredibly hard to find so the best thing is to use a few platforms (keeping the number as low as possible) that can talk to each other.


Then you can combine things in a single database with multiple tables that have ways to connect (client names and dates for example) to create your single source of truth.



Data framework: Automate the data as much as possible 


If we can't have everything in one system, let's at least find ways to automate data back and forth - meaning either between platforms or into our single source of truth database.


When selecting software, check out places like Zapier, Coupler, and Integromat.


Zapier and Integromat are great for sending individual tasks or events between systems or into a database.


Coupler is great for sending report level information into databases. 


The difference between event level and report level automations is that the report level one (Coupler) will send something like “Sales for the Month” into a database. Event level will send transactions as they happen into the database.


Report level data can be refreshed and replaced easily. The event level is a little harder, but is better and prompting an action somewhere else.


Example here… instead of sending individual invoices and expenses as they come in, the report level will send the full profit and loss for the month. As any owner knows, there are usually some changes to the Profit and Loss when the month closes so its better to capture a “finished” version instead of an “as it happens” version.


Use our Coupler link for affiliate pricing and tell them we sent you!




When you can't automate things…


Make it as simple as possible. Every software can export a CSV file of data. Uploading that to a database, manually changing a Google Sheet, etc. isn't really hard. Potentially a little annoying but if done right, it doesn't take more than 5 mins and that's normally doable in a week. 


Things that are okay to keep manual: anything that fits into that category - 

  • a simple CSV download or 

  • at most a weekly change to a manual tracker.


You can also have a manually updated tracker / translator. 


For example, if two of your data sources need to talk to each other, but you don't have matching fields, you can create an intermediary. This is common with Client names. “Pineapple Consulting” is different than “Pineapple Consulting Firm”. 


Any human looking at that will know it's the same company, but any automation or data platform will view those as different. So if you need to, you can have a translator table that's like a VLOOKUP in Excel. In one column, you have “Pineapple Consulting” and the next column is “Pineapple Consulting Firm”. Going through that translator allows you to keep both data platforms talking to each other. And if you only have a couple of new clients a month, it's easy to keep up to date.


That concludes our Data Focus and I know there's a lot of pieces there, but it does become easier relatively quickly! 


We're always happy to help and lend our expertise though! 


ACTION FOCUS

Visualize your small business data to become more data-driven


Data Visualization


If you as the owner and the new kid who started a month ago can both look at a visual of data and both understand what it's saying, you're good! That's the goal - easy to use, understand, and update.


The CEO and the new kid might have different action items from looking at the dashboard, naturally, but understanding the info that's being displayed is step 1 for a successful data visualization (aka dashboard).



Data visualization for ecommerce cohort retention


The goal should be simple, but effective visuals.


Much like a book, people start in the upper left, work their way across the top, and then move down.


So put the main KPIs at the top, and then some supporting visuals / breakdowns / trends below it that have the same KPIs as the focus of them.


The main KPIs (the sweet spot is normally 3 - 6 KPIs) should have a comparison piece to it too - a comparison to a prior period, normally month over month or year over year.


The breakdown visuals should be a dimension that makes up a main KPI (ex. revenue by client).


The trends should show enough time periods to be able to easily see a trend and any cyclical nature too. More often than not, this is a 13 month trend so you can see a rolling 12 months for a full year, but also see the same month of the previous year.


Colors are important here as they lead the eyes. Classic things like red is bad and green is good, but also grouping things by color. For example, revenue is always in blue, and expenses are always in orange.


This helps your mind easily keep things together.


If you can look at your dashboard and understand the health of your company in 30 seconds or less, you've got a great dashboard.


There are plenty of niche “rules” here but since this is a section on visualizing data, let's just show another example.



Data visualization for a quickbooks dashboard


Automate your data as much as possible


Note that we haven't even talked about AI yet. We focus a bit more on automation (at the time of writing) because we need this data to flow in an automated way. That just means that data is coming in, being ingested, and output in a consistent format.


AI is great, and can help with this (and the analysis piece) but don't neglect automation!


We covered this a bit above with Coupler, Zapier, etc but we just want the data to be as easy to update as possible. If its not easy to update… you'll never update it, let's be honest.


I'd also caution against absolute perfection too. Ideally we want the data to be 100% right, of course, but don't let a 95% correct set of data be a bad thing. 


These dashboards that are the result of your data strategy audit and framework are for decision-making and guiding the company.


Most people make decisions based on 70% of the data that's possible because the additional 30% can lead to overanalyzing, not focusing on the right thing, and most importantly, not doing it in a timely manner because getting 100% perfect is incredibly hard!!


So certainly strive for it, but don't let perfect be the enemy of great.



Wrap up


If you keep your business goals as the driver, you can create a data strategy framework to support them much easier. If you visualize the end result, your decisions become clearer, and your roadmap for the future becomes data-driven to the point where you can actually take a deep breath!


Data visualization for a clockify dashboard

About Pineapple


Pineapple is a data analytics company ready to help you become data-driven! We help analyze and visualize your data in custom dashboards so you can see your full business performance at a glance, and provide analysis to drive your strategy. Our interactive dashboards will save you time, provide deeper insights & analysis, and help you make better business decisions.


Learn more about our custom dashboards:



9 views0 comments

Comentarios


bottom of page