Your Data is a Strategic Asset
Most businesses are creating ever increasing amounts of data. Business data contains patterns and information that are the key to tomorrow’s successes. All too frequently, data is archived away. Tomorrow’s leaders realize that next to their employees, their data is the most important asset in their business. We spend time and money to create data, but neglect to leverage the information that’s contained in it. Data should be put to the best possible use in order to create a successful future.
Business leaders neglect data because there’s a knowledge gap. Many leaders simply don’t know how their data can be put to better use. Even the smallest businesses can get their data to work for them. Data contains key information and hidden insights of stories waiting to be told. Small businesses are often run based on intuition. Most leaders have had the unfortunate experience of their intuition having been proved wrong. Accessing the insights of your data can influence better decisions.
The adage “knowledge is power” holds true for every business. Making better use of your data will result in higher earnings and improved cash flows. You need to learn how.
Treat your business data as an asset, good as gold.
Stillwater Group LLC is experienced in helping companies transform their business by showing them how to monetize their data.
Pipelining Your Data
The first step in leveraging your data’s potential
Data Streams for Small Business
Creating Consolidated and Cleaned Data to Work With
For all the data being created, many business do not have “good data” to work with. What is “good data”? Good data is data that can be accessed, consolidated and cleaned for timely analysis and interpretation. Many small business struggle with having access to clean data that can can be analyzed in something like real-time. Because this is difficult, they avoid it. If data is ever looked at, it’s so old that it’s not actionable. This means many small businesses are basically flying blind. Data streams or data pipelines need to be created with the goals of security, convenient access and some effort to get the data as clean as possible when created. Real world data always needs to be scrubbed before being analyzed, but having an organized and reliable business processes to pipeline data is crucial. Subtopics in this process are onsite vs. cloud based archiving and the creation of data warehouses. Even small business are now storing a huge variety of data. To monetize the data, you need a data stream plan.Analysis Methods in Brief
To monetize your data, there are two different ways data need to be analyzed: exploratory analysis and explanatory analysis. For most small business we start with explanatory analysis, with a focus on financial analytics. This is a scientific way of looking at operational processes and seeing if they display stable or instable performance. The first task is to understand why processes are not stable and make them stable. Exploratory analysis looks at data and tries to “see” patterns that noteworthy. This involves correlations/trend discovery, surprise discovery and understanding of outliers. These kinds of analyses can provide insight to HR, sales and marketing data.Visualization is Good, but not Enough
Data needs to be curated carefully so that we see important things. Driving analysis to data visualization help our brains process complicated relationships better than staring at numbers. Data visualization is not a destination. It’s merely a intermediate milestone. To monetize data, the insights need to drive actions. Visualization does not guarantee actions. As business transform, the nuanced data that needs be visualized changes. The goal is gaining insight from data to better inform today’s decisions.
Data Analytics/ Data Science
Chart your future with analytics and predictive models of your business
Various Analytic Methods Used in Small Business Data Science
Expose and Address Unstable Processes: Time Series Data and Regression Analysis
Small business produce a rich amount of time series data. This data carries a lot hidden insights. We would expect some processes represented by time series to be correlated to each other, such a sales revenue and variable costs. This can be tested using Regression Analysis. If the correlation is very low, these two functions are not working in harmony - Why not? Addressing the poor correlation usually results in better operational performance. The skin is peeled off the onion, exposing patterns in processes that are not desirable. There are many operational processes that should have correlated time series data. This is rich space to monetize the data, once features are exposed.Get a View of the Future: Statistical Analytics and Sensitivity Analysis
Once the operational instabilities have been exposed and addressed, small business operate in a kind of “steady state”. This is huge improvement. Once stable, a small business can be modeled. This step of Predictive Modeling, is a goal of data science. Modeling is based on statistical methods, but has a kind of feedback feature where the latest results are feed back into the model to be updated. Getting timely updated data is very important to maintain the accuracy of the model. The business break even analysis can be used for a kind of sensitivity analysis to the stability of business operations. Sensitivity Analysis give you an idea of how small changes in the cost structure of the business will effect the EBITDA performance. This gives business leaders an sense of financial risk from an operations standpoint. Once in “steady state”, sales forecasts directly predict year end earnings. The Net Earnings impact of this approach has been proven to be very substantial. This is how a small business begin to monetize their data with data science.Small Business Data Science: Modeling with Machine Learning (ML) and Artificial Intelligence(AI)
Small businesses can also benefit from more advance data science methods. The sales predictions mentioned above usually usually human estimates based on intuition and market knowledge. There are more sophisticated predictive Machine Learning methods of analyzing time series data. Models can be trained with historical data and make predictions taking into account longer term trends, seasonal effect and holiday information. Errors and also predicted to provide confidence levels. Over short time periods (3monthes), these models are very accurate.There are other “use cases” in marketing for Artificial Neural Networks (ANN). Customer segmentation is used for targeted marketing campaigns. Statistical methods fail in providing insights into customer behaviors which may be used to segment them into different target groups. Neural Networks are used in conjunction with ML Clustering models to segment customer behaviors. This methods perform best with larger datasets of a customers.
AI models also have “use cases” for employee retention analysis and predictions. Employers want to measure the factors that contribute to employee retention and attrition. demographic models can predict attrition candidates so employers can proactively take steps to retain the best employees. The accuracy of these models are very dependent on the data sets available, but 85% accuracy is commonly achieved. Some of the factors contributing to retention/attrition may be inferred from bigger datasets for you to leverage in your.
The “use cases” for data science in small business are rapidly growing. With a knowledge of your business data, the possibilities of creating insights are endless. Knowledge is power for small business.
Start a Project with Stillwater Group. We Can Help Your Leverage Your Data and Bring Peace of Mind
We have expertise in all areas of Data Science to improve your business performance. The most common projects involve a combination of Business Intelligence and Statistical Methodologies to model your future. Enterprise organizations may have the larger data sets needed to support ML and AI techniques. Contact us to learn about practical Business Cases of how you can leverage Data Science to enhance the performance of your organization.