Designing a data capture strategy
Understanding what data needs to be sourced in order to implement your solution is one of the first steps when starting an analytics project. Creating a data source matrix that lists out the type of applications, databases, or external sources to be collected helps organize the development process. By better understanding what information will be collected you can begin to decide on what framework is best for your business needs. Financial data from an ERP system is going to have different requirements than a data capture strategy designed for inventory management.
Understanding the business process
This brings us to our next point. Understand the business process that you are trying to replicate with a analytics implementation. You may understand the process (after all it is your job!) but the development team tasked to design your analytic solution should be well educated on your current process. This will ensure that your needs are met and the analytic solution is aligned with the mission and strategy of the business. Are you moving your operations to the cloud? Implementing a new BI tool? By mapping out your current business process, developers will better understand the requirements for your analytics projects. Do not assume a data scientist understands your financial reporting process or how your business uniquely tracks purchase orders online. Domain knowledge amplifies the effectiveness of a analytic solution and reduces development time.
Embracing data governance
When business users start an analytics project there is often much excitement over the possibilities to come. Users look forward to engaging with beautifully built dashboards that offer dynamic capabilities and real-time insights. Managers obsess over the use of big data analytics. And executives put pressure on development teams to create visual results as a tangible sign of progress.
Unfortunately, the excitement of a streamlined reporting process or, a cool new machine learning algorithm, overshadows the importance of data governance. Data governance policies should be integrated throughout the entire analytics project life-cycle. The development team and business users should work jointly to determine the usability, integrity, and security of the proposed solution. Fortunately, there are many trusted solutions that utilize strong data governance practices. Unfortunately, there are many that do not.
Empowering project managers
The last factor we will discuss is the subject of the project manager. Very often, data analytics projects are viewed as a straightforward development project that would be traditionally carried out by an IT department. This is not always the case.
Most analytic projects require a highly cross-functional team consisting of data engineers, research scientists, IT administrators, business analysts, and support from users with domain knowledge about a particular subject, such as supply chain logistics or finance. Data science projects are no different than any other successful initiative. You need to have good project management.
Are you starting a self-service BI project? Implementing a new software suite? Productionizing a machine learning platform? Great! All scenarios warrant a dedicated resource who can act as a project manager throughout your data analytic life-cycle. Dedicated project management reduces development time and increases the success of the implementation.
Analytics can be utilized for a diverse set of use cases and are relevant to every business. From small startups to large enterprises, data analytics can help drive business outcomes and streamline existing processes. From implementing an enterprise data warehouse to self-service BI tools, data science is here to stay. Continue to read through our series as we discuss some key topics to consider when starting an analytics project.