Data science. Visualization. Analytics. What does it all mean?
Data analytics has drastically changed the landscape for business operations in recent years and it is no secret that businesses have an abundance of data available to measure. However, many organizations are challenged with understanding the data that they have available to them and how best to strategically put it to use. Effective implementation of an analytics strategy has a number of benefits from understanding customer buying preferences for targeted marketing campaigns to tracking how quickly materials are used for buying optimization. The use of analytics helps professionals to make more informed decisions and can reduce the number of resources – financial and material – needed to execute on business objectives.
What does data analytics mean?
The term data analytics encompasses a large bucket of types of analysis that can be performed across an organization to collect valuable insights. The purpose of completing an analytical project is to take disparate pieces of information and tying them together to create a shared understanding of performance, opportunities to improve, and strategies to implement.
There are four main types of analytics:
- Descriptive – What happened?
- Diagnostic – Why did it happen?
- Predictive – What will happen?
- Prescriptive – What actions should be taken?
Descriptive analytics take assess what has happened in the past and for the purpose of providing metrics on past performance. These types of metrics can be as simple as stating the number of employees that completed a required training program and how many are still outstanding.
Diagnostic analytics dive deeper into data to understand why specific events took place. In this case, an analyst supporting a sales team may be asked to determine why a sales goal was not met and the analysis may uncover that a large client did not purchase their usual order amount.
Predictive analytical models are a higher tier level in terms of complexity and incorporate statistical modeling to suggest events that are likely to occur in the future. Statistical models require larger data sets as the model needs to be able to effectively identify trends in historical data and account for any anomalies in the data set. An example of a predictive model is identifying customers that are likely to complete a repeat purchase based on the satisfaction scores of previous store visits and historical buying patterns.
Prescriptive analytics communicate specific actions to take based on statistical modeling and often advanced analytics capabilities such as artificial intelligence (AI) and machine learning. The purpose of a prescriptive model is to prevent a problem from occurring in the future or to capitalize on a current opportunity. For example, a company may be considering acquisitions to execute on a growth strategy and may leverage a prescriptive model to identify target companies based on specified criteria.
Tying Together Analytics and Strategy
The key to effectively integrating analytics into business strategy is understanding the meaning behind the insights that are gathered by analyst team. This is where storytelling comes into play. For professionals that are involved in strategy development, the story behind the data is crucial as this is the information that serves as a foundation for the business strategy. Data points that are actionable can be transformed into specific activities to drive strategy. Additionally, building a data-driven strategy facilitates tests various hypothesis to see if they hold true as the strategy is executed.
Skills to Focus On
An effective data analyst has a balanced combination of quantitative and qualitative skills. While being able to collect, manipulate, and analyze data are important skills for any analyst, the ability to tell a clear, concise, and actionable story with the insights gathered is of equal importance. This ability to tell a story involves possessing a deep understanding of the data sets and how each piece of information relates back to one or more business objectives.
If you are interested in transitioning into an analytics looking to build your resume for analytics positions, my top three recommendations for technical skills to develop are:
- Structured Querying Language (SQL)
SQL and R are both technical programming languages that allow you to retrieve data from the databases behind applications and further manipulate it for reporting and analysis purposes. The primary advantage of learning programming languages is being able to connect data from multiple sources and draw insights that would not otherwise be available by looking at the sources separately. By combining multiple data sources, analysts are able to tell more complete the stories that represent the big picture view of the analysis. There are a number of courses online that teach the syntax and commands used in both of these languages and require no prior experience to get started. Udemy is a great resource for online training courses in SQL and R that are self-paced and economically priced.
Tableau is a data visualization tool that is extremely flexible and allows users to create rich, custom visualizations using a variety of data sources. Visualizations created in Tableau are easy to create but extremely powerful to present many data points in a manner that is concise and visually appealing. Tableau offers a number of free training courses on their website and certifications are available to demonstrate proficiency in utilizing this tool.
In addition to the courses mentioned above, General Assembly offers boot camps and immersive courses in data science topics that dive deeper into in-demand skills in this field.
Adding analytics capabilities to your professional toolkit is beneficial in nearly every industry from political affairs to marketing. The complexity of the analysis that is relevant to your field will determine the skillsets that are beneficial, but having an understanding of how you can incorporate analytics into your everyday work is beneficial as the professional landscape increasingly becomes more data-driven. Think of any business question as a puzzle that needs to be solved and identify opportunities to incorporate data into your answer for a more powerful response.