How should analytics help businesses
There are many examples of successful application of analytics and data science in business. However, when first exploring if the analytical path is right for your business, it is necessary to have a good idea of what to expect, if and when you actually adopt it.
Businesses have huge amounts of data from many sources – ERP, CRM, Manufacturing, Forecast Spreadsheets, R&D and Logistics systems, to name a few. The source of this data may be internal to the business or perhaps external (Twitter feeds, commodity prices, website traffic stats). The data can be segmented into two broad categories: structured and unstructured. Although employees may be able to view and consider unstructured data, they lack the ability to easily correlate it. On the other hand, with structured data, they often lack the capability to efficiently analyze them. This would include data as wide ranging as machine tool logs, financial transaction records, and spreadsheets, etc. Another important point to keep note of is that, quite often combining structured and unstructured data analysis can yield value building business insights.
With all this background, ground zero for a business considering the analytical path is to formulate a clear strategy for using their data. A clear vision of the desired business impact must shape the integrated approach to data sourcing, model building, and organizational transformation.
Choose the Right Data
Data is often not in a format decision makers can use to make key decisions. Businesses also need to be innovative about the potential of external and new sources of data. For example, social media generates loads of nontraditional, unstructured data in the form of conversations, photos, and video. Add to that the streams of data flowing in from sensors, monitored processes, and external sources ranging from local demographics to geographic locations to weather forecasts. What decisions could you make if you had all the information you need in the format you need it?
Consider Models That Predict and Optimize Business Outcomes
Business performance improvements and competitive advantage arise from statistical and mathematical models that allow executives to predict, optimize and proactively alter outcomes. Importantly, effective approaches to building predictive business models should start with identifying a business opportunity and determining how the models can improve performance. Hypothesis-led models generate faster outcomes and ties the models in practical data relationships that are better understood by managers.
What to expect from the outcome of analytics undertakings
Whether you decide to handle this with your own team or seek outside help, the process is the same. Below is the process we adopt when helping our customers.
Step 1: Analytics should be a means to a business end for you. If you don’t know where your opportunities or needs lie, then we will help you to identify a set of business priorities.
Step 2: From there, we evaluate your data architecture (what data you store and retrieve; and how you do it) to define a low cost, high impact project in line with your business objectives.
Step 3: Build any explanatory or predictive models that can deliver the insights which will help accomplish your business objectives.
Step 4: Once we’ve conducted the work, your solution can be provided to you as a secure cloud based dashboard or app for accessibility. For additional security, they can be hosted internally within your own network or stand-alone computers. The app or application will be a purpose built tool based on your data for the operating system or platform of your choice. You can then use the app to model or predict outcomes from the inputs you provide.
Step 5: Maintenance of the app will reconfigure the underlying model per the latest and greatest data to ensure it is up to date and giving you value on an ongoing basis.
At SimaFore, our mission is to democratize analytics. We make the tools affordable and accessible for just about any business or organization.