This Helps Me How?

Save Time and Money with Streamlined Workflows for Your Staff.

Automate data capture and eliminate duplicate manual entry.

Make better, faster decisions with TIMELY DECISION DATA™ from your existing software systems.

Increase operating cash flow in your business.

Business Intelligence (BI) from your Existing Software

If your disconnected software systems and operational inefficiencies are stunting growth of your small business, then we can help.

Why?

Jake is a serial entrepreneur and fellow small business owner with similar disconnected systems who figured out efficient, automated ways to extract, shape and report TIMELY DECISION DATA™ from those systems to drive better decisions for his company and for his clients.

This saved time and money for him and his clients. We can do the same for you.

Browse the case studies below to get an idea of how custom-built automations and data modeling tools can deliver similar results for you.

Case Study 1 - Revenue JE Import

It sounds simple enough - we built a data model to produce a monthly revenue journal entry import file. The raw data came from a clunky billing system report printed each month-end as a PDF with more than 150 pages...and then transferred BY HAND to the general ledger.

Three thousand rows of debits and credits.

Three days to enter manually.

Every month.

The data model we built replaced that three-days-per-month manual journal entry process with a ten-minutes-per-month journal entry import to post monthly billing data to the general ledger in Quickbooks.

This saved 14% of the accounting manager’s time each month.

Case Study 2 - Network Circuit Utilization Model

We built a circuit utilization data model transforming millions of log entries into “danger zone” graphs to predict when communications circuits would reach maximum capacity.

This revealed where the company would have to add capacity to prevent buffering for its customers and to reduce strain on its network.

The software that provided the raw data did not help them USE that data.

They tried to analyze the raw data, but couldn’t see patterns clearly - until the circuit utilization model shaped that same raw data into something they could use and something that prevented them from getting caught with insufficient bandwidth.

See the Testimonials section for more about this custom tool.

Case Study 3 - Work Comp Cleanup

We cleaned, shaped and combined messy payroll data into a single, complete and summarized report for a client's work comp policy period and premium audit.

The raw data came from two partial periods and two different payroll service providers, with mismatched job titles and incorrect or nonexistent work comp codes.

This custom data model provided total earnings by employee and by work comp code to eliminate estimation and guesswork and minimize premiums assessed by the work comp insurance carrier.

Case Study 4 - Cable TV Channel Fees Analysis in Power BI

This data model (built and published to Power BI) allowed the cable TV provider client to drill into various service tiers and individual cable channels to see which channels were driving most of the company's cost increases from the content providers.

It reduced the labor intensity of this process and gave the client better visibility to an otherwise unwieldy exercise. It allowed him to sort and filter by content type (sports, news, local broadcast), by payment type (direct to programmers or through the trade association) and by local market.

 

This gave him a better understanding of cost drivers and provided data-driven support when explaining necessary annual price increases to subscribers.

See the Testimonials section for more about this custom tool.

Case Study 5 - Dynamic Subscriber Dashboard

We built a custom subscriber dashboard summarizing hundreds of thousands of data records with raw data pulled in CSV files at the end of each week.

This dynamic report cleaned, shaped and aggregated all of that weekly raw data so that the client could filter on the fly (by date, time period, department, division, market, product type) to see subscriber growth and rates of change between any historical and present periods.

This dynamic, interactive dashboard replaced a useless, static, uneditable, 50-page PDF report from the billing system.

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