Data Analytics Services

Data Analytics Services

GHJ offers a variety of services relating to acquiring, cleaning, maintaining, analyzing and visualizing data as well as certain automation services.

Service Offerings

GHJ helps clients discover, store, share and analyze data using the latest technologies.

Automation assists in cutting down the time and resources spent performing highly repeatable menial tasks that could be done faster and more reliably by a computer. The opportunities in automation include data preparation/cleaning, transformation, pipelining and reporting.

    • Delays from manual reporting
    • Errors in reporting from manual generation of reports
    • Diversion from important responsibilities to perform menial administrative tasks
    • Leverage automation opportunities to improve efficiencies and reduce menial data-related tasks

Business Intelligence leverages data to build KPIs and visualizations such as dashboarding and interactive visualizations. These tools build a data narrative that supports descriptions and diagnostics. In addition, business intelligence includes data acquisition and extraction, which means getting your data where it needs to go and connecting separate systems to get them “talking to each other.”

    • Data is collected but not easy to understand or use
    • Large tables of data are hard to follow and interpret
    • Report formatting is changing frequently and KPIs and metrics are not kept in a way to be reviewed over time
    • Organize and visualize data so that it is easy to interpret
    • Assist in standardizing KPIs in a way that allows them to be comparable over time, making it easier to spot themes and trends
    • Generate dashboards for interactive data exploration which allows for a higher degree of customizability and an easier way to compare desired metrics quickly

In order to get the most from your data, you need a platform run by a liaison who is both familiar with your business needs and well-versed in data analytics.

    • The team lacks the technical knowledge of what to do without proper support from the vendor
    • Excessive back and forth with the vendor has dramatically slowed implementation
    • A vendor who is having difficulty understanding the business’ problems and opportunities
    • A vendor who is overly technical in their communication to the point of obstructing progress
    • Maximize the usefulness of your analytics platform by assisting in data extraction, transformation, modeling, KPI building and communicating business needs in a technical manner

Data Science is data modeling on a large scale for intricate modeling and machine learning. This includes unstructured data analysis and unsupervised learning models. It is best served for those with an abundance of available training data who are seeking a more nuanced result (an example of a very sophisticated model would be a credit card’s automated fraud detection model where there is an abundance of transaction data and relatively low stakes for false positives/overfitting).

    • A lack of technical expertise to implement a model with your large dataset
    • Unstructured data (e.g., text) in need of analysis
    • A need for a nuanced effect that would not be prone to overfitting (tailoring a model too much to the training data such that it is no longer generalizable)
    • Train and implement learning models
    • Perform clustering analysis on unstructured data, which shows relationships that may otherwise be difficult to quantify (i.e., spending habits of different groups of customers)
    • Perform sentiment analysis on unstructured data, which helps make a large volume of text data (i.e., social media) more manageable and understandable

For data modeling to be useful and informative, the underlying data has to be uniform, well cleaned, prepared and well documented. Data governance provides documentation, standardization and guidelines for what raw data means and how it will be entered into storage for use in modeling.

A simple example of a data governance issue would be storing payment data in multiple currencies without converting to USD (or documenting additional currency information). This will cause issues immediately as real time data is not being assessed along with reporting down the line because the currency used will need to be determined, as well as the conversion rate on the date/time of payment.

    • Multiple sources of data with conflicting/inconsistent results and difficulty determining which should be trusted as a primary source
    • Data stored in different formats that hinders use or generates unwanted redundancies
    • No documentation for data that explains what each stored item is and the expected format
    • Document source veracity and priority to keep multiple data sources interacting in an understandable way
    • Standardize data storage methodologies and document procedures to keep data sources clean and ready to be used
    • Generate a data dictionary with expected formats and explanation of collected data
    • Enforce governance via automation and platform-based checking to ensure compliance and achieve a state of usable, reliable data


Specialized Service Team