Business success is measured by the number of sales and revenue it generates over time.
But how you reach that point with your business is just as crucial.
In other words, the devil is in the details. Every factor contributes to your business’ success, from the channels driving highly targeted traffic to your website to the minute changes on your site pages.
And the best way to fully understand how your business generates revenue is by gathering and analyzing big data.
However, your job as a project manager or marketer is not just collecting and visualizing data from your analytics toolset. You must understand how your tools gather the information and why it gathers a specific data set.
Knowing how these things work is critical to unearthing the actionable insights that will propel your business to greater heights.
Identify and Capture the Right Data
Having a full grasp of the business type you’re running enables you to determine the kind of data you must collect and how you can gather them.
For example, if you’re running an agency, you must look at metrics relevant to growing your client pool, such as qualified leads, customer acquisition cost, net margin, and others.
At the same time, running a SaaS requires a different set of software metrics to look at. Examples include code quality, user satisfaction, and quality of the delivered solution.
More importantly, you can track and analyze employee performance to maximize productivity and increase output.
We’re not just referring here to how much time your employee spent on a task – we’re also talking about the different aspects of your organization. For example, this includes human resources (How effective is your HR in finding the right people for the job?) and customer service (Is my customer support team doing everything it takes to keep my customers happy?).
You’ll then know which tools will help you gather the correct data set for your business. For example, predictive analysis and statistical modeling tools like Zoho Analytics allow you to forecast your marketing campaigns in advance.
You can also implement data gathering methods that require active participation from your audience. For example, you can embed a survey on your website for visitors to answer, or you could email your subscribers a link to a Likert scale that grades their satisfaction level with your service.
Collecting these product-marketing fit metrics gives you a better understanding of who your actual target market is and how you promote your business to these people.
Organize Your Data Harvesting
You must determine where your data warehouse structure is stored and how data is loaded from the structure.
Knowing where and how big data of your enterprise is stored is crucial in the long run, especially if you want to test the data transfer and migration effectiveness. Both could affect the quality of data gathered from your sources to your data center.
To ensure that you have the proper infrastructure for collecting and storing big data, this is where the importance of ETL testing is emphasized more than ever.
Running the most appropriate ETL testing method allows your organization to capture any errors that might stem from the extraction and transfer of big data and determine whether it’s a human or system error. From here, you must troubleshoot the process and iron the kinks before rerunning the test.
More importantly, having a fully functional data storage structure gives you complete control over who gets access to your information. Identifying an access control permission model for your enterprise is the first step to creating a secure data structure. From here, you must inform the authorized personnel who will have access to your data and outline their responsibilities.
Select a Data Analytics Programming Language
While most marketers focus on the results produced by analytics tools, it’s just as important to learn how these tools help gather the data from various online channels.
Below are some of the most popular programming languages your organization must consider learning moving forward:
- C++ – Enhances processing speed by writing extensive data libraries and frameworks, resulting in faster gathering and computation of big data projects.
- Java – Remains popular among the big data community due to its flexibility and broad user base. It is used primarily for writing product code to scale big data algorithms.
- JavaScript – Processes massive amounts of data without worrying about errors. It can create multiple-choice and conditional formulas to automate decision-making in a big data project.
- Python – Similar to JavaScript, Python creates big data projects from start to finish with a low margin for error. The programming language’s popularity includes being object-oriented, open-sourced, and developing code at high speed. Due to its popularity, you should grab any of the Coursera Python courses available to jumpstart your big data initiative.
- SQL – Used to execute various data operations as a key API for big data projects. It is beneficial for managing unstructured data containing multiple data models.
The types of programming languages you plan on implementing in your big data are a critical step in hiring a dedicated product team for your business.
For instance, getting people knowledgeable in Python and C++ to extract and manage data is a must. This way, your organization can proceed with your program instead of needing to first get up to speed with the programming language.
Make Calculated Decisions
Data-driven decision-making is grounded on hard, irrefutable facts. The challenge is to determine which methods and tools to pursue when making decisions that will alter the course of organizations for the better.
One of the better methods available is a weighted decision matrix, a decision-making tool used to choose between different options often discovered after interrogating big data or studying analytics.
The technique weights the factors involved in a choice (e.g., staying on your current hosting or moving to the best Magento hosting) so that the most important factors carry the most value.
Each option is scored against each factor involved, and the scores are multiplied by the weighting and added together.
This technique forces the decision-maker to think through the factors involved and their relative importance to one another. The result is a decision that relies less on emotion and more on an objective approach.
Scale and Repeat
After deciding your course of action using big data, you must apply the changes and run everything back for another round of testing.
Over time, you will have collected big data about the changes and see if they made any significant changes in your organization for the better.
When scaling your efforts this way, you also want to see if your current testing affects how you collect, store, and analyze big data.
The goal of scalability for your systems and processes is to untangle big data by taking as much human intervention out of the equation.
There are three ways to ensure the success of your data scalability efforts:
- Store only the information you have in your data warehouse. Collecting information you have no use for in the future will lead you to run into cost and performance issues, especially with online analytical process (OLAP) databases.
- Eliminate repetitive or unnecessary queries over large databases for the same reasons above. To do this, enter the materialized view to pre-compute data from specific queries instead of the entire database.
- Always strive for flexibility with your data. While the ETL framework is sufficient in most cases, you may not want to hand over all your data to a warehouse. However, you must ensure your team can make that transition first.
Conclusion
Analyzing big data is a momentous undertaking, but the payoff can be significant to your bottom line if you do it right. Follow the tips above to help your company move through each task in the big data analytics process systematically and get on the path to success more quickly.
