Data Analytics Framework

In this day and age, data is foundational to virtually every enterprise across the world. Organizations at this scale produced a significant amount of the 79 zettabytes—i.e., 79 trillion gigabytes—of big data estimated by Statista to have been generated in 2021, and thus are responsible for the lion's share of its usage, processing, and storage.

Data Analytics frameworks are integral to all large-scale data management and optimization efforts. They combine efficient processes with cutting-edge data technologies to create insight-rich strategies for enterprise operations. 

Older models didn't look at the organizations' needs as a whole-—thereby siloing data and creating roadblocks to efficiency. Understanding modern data analytics frameworks and implementing them successfully will be critical for any business looking to get ahead of the curve.

A data analytics framework is a concrete system for managing data analytics efficiently and effectively. But the term itself is used in multiple ways. 

For practical purposes, think of a data analytics framework as a union of processes and technologies. The specific guidelines and solutions used will vary, often widely, between organizations. But the fundamental goal of data analytics frameworks is consistent—to help enterprises use analytics in a way that derives the greatest possible value from the information. Lacking such a framework, and taking a piecemeal, disorganized approach to data analysis, is not an option in today's business world.

Organizations typically base their data analytics frameworks on a clearly defined purpose. This goal can be basic at first—something like, "What business outcomes do we hope to achieve for our enterprise with data analytics?" From there, data teams branch out into more specific operations.

This is one of the most common use cases for analytics and the frameworks supporting them. Enterprises need to be constantly cognizant of everything that affects their bottom line, and gathering key performance indicators (KPIs) and assessing them is how they maintain awareness.

A data analytics framework provides data teams with processes and tools for wide-ranging performance assessments—e.g., of profitability across different business units—as well as narrower operations, like monitoring a customer-facing app's daily active users, engagement, and new user adoption.

Today, it's impossible to develop a new product in a vacuum. Development must be informed by data, including historical sales figures, KPIs regarding competitors' success or failure with a similar product, demand analysis, projections of potential product failures, and much more. 

The ability of many modern devices—ranging from smartphones and medical wearables to modern cars—to collect consumer behavior data adds another dimension of insight developers can draw upon. Data analytics frameworks ensure that product teams can leverage this disparate information, drawing insights to learn from past mistakes and determine better product design strategies for the future.

With predictive maintenance systems in place, manufacturers and other heavy industrial businesses can assess machine health, project the likelihood of failure, and schedule priority repairs when needed. 

This helps minimize equipment downtime and keep production schedules on track. Data analytics frameworks provide the structure analysts need to gather all the data necessary to make these predictions—equipment age, number of previous repairs, indicators of wear and tear, overall effectiveness, and so on.

To achieve optimal results using a data analytics framework, support it with cutting-edge technologies and solutions:

Given that modern enterprise data management is significantly driven by cloud trends, your framework should be cloud-ready—but without sacrificing your on-premises data infrastructure. 

A hybrid multi-cloud deployment will give you the most fleibility in this regard, particularly if your organization's analytics needs involve real-time stream processing.

Also, data integration from all relevant sources is a must, and the solutions and tools you use as part of your framework should support this. 

An analytics platform with warehousing capabilities, for eample, can provide a firm foundation for integration.

Using object storage infrastructure to create a data lake that works alongside a data warehouse ensures that all structured, unstructured, and semi-structured data can be properly formatted and categorized for later processing and analysis.

If you successfully deploy a data analytics framework based on sound principles of data science and supported by agile, reliable technologies, your enterprise has the potential to realize numerous benefits. Here are some of the most notable:

A cloud-centric analytics framework allows for the coeistence of multiple data types and permits multiple analysis methods. Together, this helps significantly speed up the integration and effective use of data, cutting down on time to analyze and minimizing performance bottlenecks. Thus, less time is spent on processing, preparing, and reconfiguring data, meaning more time can be devoted to applying data in innovative ways.

The speed of integration and use also allows for real-time data processing. This can improve customer service, facilitate more effective internal collaboration and innovation, and boost operational efficiency.

Adopting a cutting-edge, cloud-based data analytics framework gives your enterprise the ability to store, access, and use all your data without reformatting, duplicating, or moving it around. 

Instead of having data spread out and in multiple incompatible formats, you can get straight to analysis, application, and innovation. This, ultimately, will support an end-to-end view of the business and create a single source of truth (SSOT). 

In an unpredictable business environment where organizational needs and customer demands can change instantly, a data analytics framework that allows you to scale up or down on a dime is invaluable. 

That's eactly what you get with a cloud framework. This scalability can also drive cost savings. The tools used in more traditional analytics frameworks can be epensive or involve rigid pricing models, but cloud analytics solutions allow you to pay only for what you use.

There are many approaches to business analytics and countless tools that support them, and the market will grow wider in the near future. Eamples of key trends to watch include:

The market for self-service reporting continues to epand as more business users grow interested in leveraging analytics without needing the epertise of a data scientist, analyst, or engineer.

Deep learning, the advanced form of machine learning (ML) based in multi-layer neural networks, will slowly proliferate as more enterprises attain the resources necessary to support its computations.

Researchers project adoption of the data fabric concept to rise due to increasing interest in real-time streaming analytics.

Walmart is looking to open new brick and mortar stores in new parts of South Asia. Since every geography has different requirements and different demand for products, it is necessary to be aware of such variables. Hence, Walmart wants to perform an in-depth analysis of different factors which need to be considered before proceeding ahead. Discuss with you group mates and list below:

Key Challenges for South Asian Retailers they’re facing currently?

What customer specific initiatives can be kept in mind alongside the planning?

 

Data is crucial in today’s digital world. As it gets created, consumed, tested, processed, and reused, data goes through several phases/ stages during its entire life. A data analytics architecture maps out such steps for data science professionals. It is a cyclic structure that encompasses all the data life cycle phases, where each stage has its significance and characteristics.

The lifecycle’s circular form guides data professionals to proceed with data analytics in one direction, either forward or backward. Based on the newly received information, professionals can scrap the entire research and move back to the initial step to redo the complete analysis as per the lifecycle diagram for the data analytics life cycle.

However, while there are talks of the data analytics lifecycle among the eperts, there is still no defined structure of the mentioned stages. 

You’re unlikely to find a concrete data analytics architecture that is uniformly followed by every data analysis epert. Such ambiguity gives rise to the probability of adding etra phases (when necessary) and removing the basic steps. 

There is also the possibility of working for different stages at once or skipping a phase entirely.

Yet, suppose, there is ever a discussion about the stages of the data lifecycle. In that case, the below-listed phases are likely to be present, as they represent the fundamentals of almost every data analysis process.

Everything begins with a defined goal. In this phase, you’ll define your data’s purpose and how to achieve it by the time you reach the end of the data analytics lifecycle.

The initial stage consists of mapping out the potential use and requirement of data, such as where the information is coming from, what story you want your data to convey, and how your organization benefits from the incoming data. Basically, as a data analysis epert, you’ll need to focus on enterprise requirements related to data, rather than data itself. Additionally, your work also includes assessing the tools and systems that are necessary to read, organize, and process all the incoming data.

Essential activities in this phase include structuring the business problem in the form of an analytics challenge and formulating the initial hypotheses (IHs) to test and start learning the data. The subsequent phases are then based on achieving the goal that is drawn in this stage.

This stage consists of everything that has anything to do with data. In phase 2, the attention of eperts moves from business requirements to information requirements.

The data preparation and processing step involve collecting, processing, and cleansing the accumulated data. One of the essential parts of this phase is to make sure that the data you need is actually available to you for processing. The earliest step of the data preparation phase is to collect valuable information and proceed with the data analytics lifecycle in a business ecosystem. Data is collected using the below methods:

Data Acquisition: Accumulating information from eternal sources.

Data Entry: Formulating recent data points using digital systems or manual data entry techniques within the enterprise.

Signal Reception: Capturing information from digital devices, such as control systems and the Internet of Things.

After mapping out your business goals and collecting a glut of data (structured, unstructured, or semi-structured), it is time to build a model that utilizes the data to achieve the goal.

There are several techniques available to load data into the system and start studying it:

ETL (Etract, Transform, and Load) transforms the data first using a set of business rules, before loading it into a sandbo.

ELT (Etract, Load, and Transform) first loads raw data into the sandbo and then transform it.

ETLT (Etract, Transform, Load, Transform) is a miture; it has two transformation levels.

This step also includes the teamwork to determine the methods, techniques, and workflow to build the model in the subsequent phase. The model’s building initiates with identifying the relation between data points to select the key variables and eventually find a suitable model.

This step of data analytics architecture comprises developing data sets for testing, training, and production purposes. 

The data analytics eperts meticulously build and operate the model that they had designed in the previous step. 

They rely on tools and several techniques like decision trees, regression techniques (logistic regression), and neural networks for building and eecuting the model. 

The eperts also perform a trial run of the model to observe if the model corresponds to the datasets.

Remember the goal you had set for your business in phase 1? Now is the time to check if those criteria are met by the tests you have run in the previous phase.

The communication step starts with a collaboration with major stakeholders to determine if the project results are a success or failure. 

The project team is required to identify the key findings of the analysis, measure the business value associated with the result, and produce a narrative to summarise and convey the results to the stakeholders.

As your data analytics lifecycle draws to a conclusion, the final step is to provide a detailed report with key findings, coding, briefings, technical papers/ documents to the stakeholders.

Additionally, to measure the analysis’s effectiveness, the data is moved to a live environment from the sandbo and monitored to observe if the results match the epected business goal. If the findings are as per the objective, the reports and the results are finalized. 

However, suppose the outcome deviates from the intent set out in phase 1then. You can move backward in the data analytics lifecycle to any of the previous phases to change your input and get a different output.

A Coffee & Fast Food Chain is planning trying to device new ways of better customer engagement to improve overall sales and take over business from their competitors. It has recently been found their competitors are launching new flavours of brewed coffee, ice creams and a few more enticing items.

Being a Merchandising Business Analyst, how would you:

Use internal data to help stakeholders create a plan for net few months to match with your competitors?

What customer campaigns can you create to improve engagement?

Data-driven decision-making (sometimes abbreviated as DDD or DDDM) is the process of using data to inform your decision-making process and validate a course of action before committing to it.

In business, this is seen in many forms. For eample, a company might:

Collect survey responses to identify products, services, and features their customers would like

Conduct user testing to observe how customers are inclined to use their product or services and to identify potential issues that should be resolved prior to a full release

Launch a new product or service in a test market in order to test the waters and understand how a product might perform in the market

Analyze shifts in demographic data to determine business opportunities or threats

Google maintains a heavy focus on what it refers to as “people analytics.” As part of one of its well-known people analytics initiatives, Project Oygen, Google mined data from more than 10,000 performance reviews and compared the data with employee retention rates. 

Google used the information to identify common behaviors of high-performing managers and created training programs to develop these competencies. 

These efforts boosted median favorability scores for managers from 83 percent to 88 percent.

After hundreds of Starbucks locations were closed in 2008, then-CEO Howard Schultz promised that the company would take a more analytical approach to identifying future store locations.

Starbucks now partners with a location-analytics company to pinpoint ideal store locations using data like demographics and traffic patterns. 

The organization also considers input from its regional teams before making decisions. Starbucks uses this data to determine the likelihood of success for a particular location before taking on a new investment.

Amazon uses data to decide which products they should recommend to customers based on their prior purchases and patterns in search behavior. 

Rather than blindly suggesting a product, Amazon uses data analytics and machine learning to drive its recommendation engine. 

McKinsey estimated that, in 2017, 35 percent of Amazon’s consumer purchases could be tied back to the company’s recommendation system.

Once you begin collecting and analyzing data, you’re likely to find that it’s easier to reach a confident decision about virtually any business challenge, whether you’re deciding to launch or discontinue a product, adjust your marketing message, branch into a new market, or something else entirely.

Data performs multiple roles. On the one hand, it serves to benchmark what currently eists, which allows you to better understand the impact that any decision you make will have on your business.

Just because a decision is based on data doesn’t mean it will always be correct. While the data might show a particular pattern or suggest a certain outcome, if the data collection process or interpretation is flawed, then any decision based on the data would be inaccurate. This is why the impact of every business decision should be regularly measured and monitored.

When you first implement a data-driven decision-making process, it’s likely to be reactionary in nature. The data tells a story, which you and your organization must then react to.

While this is valuable in its own right, it’s not the only role that data and analysis can play within your business. Given enough practice and the right types and quantities of data, it’s possible to leverage it in a more proactive way—for eample, by identifying business opportunities before your competition does, or by detecting threats before they grow too serious.

There are many reasons a business might choose to invest in a big data initiative and aim to become more data-driven in its processes.

One of the most impactful initiatives, according to the survey, is using data to decrease epenses. Of the organizations which began projects designed to decrease epenses, more than 49 percent have seen value from their projects. Other initiatives have shown more mied results.