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Last Updated on October 20, 2021 by Rovamedia
Data analytics is the science of analyzing raw data to make conclusions about that information. Effective data collation, combined with analytics, helps companies stay competitive when demand changes or new technology is developed. It also helps them anticipate market demands to provide the product before it is requested.
Data analysis is a process of inspecting, cleansing, transforming, and modeling data to discover useful information, informing conclusions, and supporting decision-making. Data analytics helps individuals and organizations make sense of data. Data analysts typically analyze raw data for insights and trends. They use various tools and techniques to help organizations make decisions and succeed.
There are various types of data analysis including descriptive, diagnostic, prescriptive, and predictive analytics. Each type is used for specific purposes depending on the question a data analyst is trying to answer. For example, a data analyst would use diagnostic analytics to figure out why something happened.
What are the Elements of Data Analytics?
1. Set Clear Measurement Priorities
This step breaks down into two sub-steps:
– Decide what to measure, and
– Decide how to measure it.
A. Decide What To Measure
Using the government contractor example, consider what kind of data you’d need to answer your key question. In this case, you’d need to know the number and cost of current staff and the percentage of time they spend on necessary business functions. In answering this question, you likely need to answer many sub-questions (e.g., Are staff currently under-utilized? If so, what process improvements would help?). Finally, in your decision on what to measure, be sure to include any reasonable objections any stakeholders might have (e.g., If staff is reduced, how would the company respond to surges in demand?).
B. Decide How To Measure It
Thinking about how you measure your data is just as important, especially before the data collection phase, because your measuring process either backs up or discredits your analysis later on. Key questions to ask for this step include:
- What is your time frame? (e.g., annual versus quarterly costs)
- What is your unit of measure? (e.g., USD versus Euro, USD versus Naira)
- What factors should be included? (e.g., just annual salary versus annual salary plus cost of staff benefits)
2. Collect Data
With your question clearly defined and your measurement priorities set, now it’s time to collect your data. As you collect and organize your data, remember to keep these important points in mind:
- Before you collect new data, determine what information could be collected from existing databases or sources on hand. Collect this data first.
- Determine a file storing and naming system ahead of time to help all tasked team members collaborate. This process saves time and prevents team members from collecting the same information twice.
- If you need to gather data via observation or interviews, then develop an interview template ahead of time to ensure consistency and save time.
- Keep your collected data organized in a log with collection dates and add any source notes as you go (including any data normalization performed). This practice validates your conclusions down the road.
3. Define Your Questions
In your organizational or business data analysis, you must begin with the right question(s). Questions should be measurable, clear, and concise. Design your questions to either qualify or disqualify potential solutions to your specific problem or opportunity.
For example, start with a clearly defined problem: A government contractor is experiencing rising costs and is no longer able to submit competitive contract proposals. One of many questions to solve this business problem might include: Can the company reduce its staff without compromising quality?
What are the Importance of Data Analytics?
We’ve seen the elements of Data Analytics, its principles, lets take a closer look at the importance of Data Analytics as it affects various areas of a Brand and Business.
- Identifying Opportunities: Data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers.
- Gaining Insights: Data analytics help in analyzing the value chain of business and gain insights. The use of analytics can enhance the industry knowledge of the analysts. The data analytics tools used by our researchers, analysts, and engineers for business organizations help to access the data efficiently.
- Customer Relationships: By understanding customers’ needs, organisations will be able to optimize the customer experience and develop longstanding relationships using knowledgeable insights from Data Analytics.
- Competition Alert: Effective data collation, combined with analytics, helps companies stay competitive when demand changes or new technology is developed. It also helps them anticipate market demands to provide the product before it is requested.
- Unique Customer Interactions: Being able to react in real time and make the customer feel personally valued is only possible through advanced analytics. Big data offers the opportunity for interactions to be based on the personality of the customer.
- Pro-activity and Anticipating Needs: By sharing data with businesses, customers expect companies to know them, form relevant interactions, and provide a seamless experience across all touch points. By understanding customers’ needs, organisations will be able to optimize the customer experience and develop longstanding relationships.
- Delivering Relevant Products: Effective data collation, combined with analytics, helps companies stay competitive when demand changes or new technology is developed. It also helps them anticipate market demands to provide the product before it is requested.
- Personalisation and Service: Companies need to be extremely responsive to cope with the volatility created by customers engaging via digital technologies today. Being able to react in real time and make the customer feel personally valued is only possible through advanced analytics. Big data offers the opportunity for interactions to be based on the personality of the customer. It does this by understanding customer attitudes and considering factors such as real-time location to help deliver personalization in a multi-channel service environment.
Conclusion: Data Is Valuable
In today’s marketing world, decisions are no longer guided just by hypotheses and past experience. Influential marketing ideas are now determined by analytics and big data. By utilizing past data and predictive analytics, businesses can now generate a better return on investment (ROI) and provide insights that can lead to effective business strategies and decisions within an organization, not just in the marketing department but across teams.
Having accurate data is essential for making effective marketing decisions, but having too much data can actually harm your marketing strategy if not utilized correctly. You should start with your key performance indicators (KPIs) and work backward.
Key performance indicators represent measurable values that give an indication of campaigns’ performance. All decisions for marketing initiatives should have a goal whether it be more visitors to the site, registrations, email collections, click-to-calls, etc. Whatever the goal is, your KPIs should be helping to support the objective.