You Can No Longer Ignore Data Analytics

What do you mean by Data Analytics?

Data Analytics refers to the process of collecting, organizing, interpreting and extracting the useful insights from the raw facts and figures in the huge amounts of data generated by a business on a daily basis. The main responsibility of the data analyst is to translate the burgeoning data into useful perceptions and then into valuable information, assisting the organizations to make smart decisions based on data instead of relying on instincts.

How important is Data Analytics?

In today’s world, accumulation and storage of the massive amount of data streaming into an organization’s databases has become a concern. Volumes have increased to millions and millions of gigabytes. Companies have moved far from just directories, datasheets, and documents.

But at the end of the day, it is almost impossible to analyze and interpret all this data. This data includes not only text but videos, photos, sound recordings and sensor data.

The data streaming into an organization’s data warehouses is increasing at an exponential pace and is expected to grow by up to 50 zettabytes by 2020. A zettabyte is over 931 million gigabytes! Each and every action we do online leaves a digital trace. Every single action we make when we go online such as online shopping, chatting with the friends through social media applications or using GPS equipped smartphones generates data which businesses mine for information. We basically leave digital footprints with every action we take digitally involving the use of the internet.

The amount of data generated is growing rapidly and this requires the use of advanced technology and tools specifically designed for the analysis and interpretation of this enormous amount of data. This is where data analytics come to the rescue. The thorough and in-depth analysis of this “Big Data” requires the use of data analytics software such as Python, SAS, R and Hadoop which were developed specifically for handling Big Data.

Data analysis using these tools with mathematical and statistical algorithms will further assist an organization with developing good decision-making processes and allow it to respond to customer queries rapidly, resulting in an increase in goodwill for the organization. The high accuracy in Big Data analysis also ultimately helps the organizations to increase profits and lower costs.

Developing new products based on ongoing market trends can be done only when Big Data is accurately and precisely analyzed. Customer satisfaction matters a great deal if a business wants to be successful and popular with its target audience. The market trends and customer preferences must be analyzed properly and specifically so as to develop products which are trendy and acceptable to the target audience. The data findings may even assist companies in taking advantage of new market revenue opportunities and ultimately improve customer satisfaction, thereby enhancing the operating efficiency and profitability of the company.

Developing Innovative Products

Phase 0: Feasibility Analysis

The goal of this phase is to identify existing technology to achieve the intended high-level function. If technology can be purchased as opposed to developed, the scope of subsequent development phases changes.

Simply put, product development companies research and assess the probability that the current technology can be used to reach the intended functionality of the product. By doing this, the development efforts are reduced, which in financial terms represent a great reduction in development costs.

Moreover, if the technology is not yet available, then the assessment can result in longer development cycles and the focus moves into creating the new technology (if humanly possible) that can accomplish the functionality of the product.

This is an important part of the in any product development process because it is safer and financially responsible to understand the constraints that a product can have prior to starting a full development cycle. A feasibility study can cost between 7 -15 thousand dollars. It might be sound very expensive for some, but when it is much better than investing $100k+ to end up with a product that no manufacturer is able to produce.

Phase 1: Specification or PRD (Product Requirements Document) development

If your product is feasible, congratulations! you are a step closer to creating your product and you can move into documenting what is going to go into the product itself, aka the guts (product objective, core components, intended end-user, aesthetics, User interphase, etc).

In this phase, product design and engineering focus on documenting the critical functionality, constraints, and inputs to the design. This is a critical step to keep development focused, identify the high-risk areas, and ensure that scope creep is minimized later.

This document will help you communicate the key features of your product and how they are supposed to work to all members of your team. This will ensure that you keep everyone involved on the same page.

Without one, you are more likely to stay off track and miss deadlines. think about the PRD as your project management breakdown structure (BDS)

Phase 2: Concept Development

Initial shape development work identifies options for form, as well as possible approaches for complex mechanical engineering challenges. Initial flowchart of software/firmware also happens here, as well as concept design level user interface work. Aesthetic prototypes may be included in this Phase, if appropriate. Prototype in this phase will not typically be functional.

Phase 3: Initial Design and Engineering

Based on decisions made at the end a concept development phase, actual product design and engineering programming can start. In this phase, Level 1 prototypes are often used to test approaches to technical challenges.

Phase 4: Design Iteration

This part of the project is where we focus on rapid cycles, quickly developing designs and prototypes, as the depth of engineering work increases. This phase can include Level 2 and 3 prototypes, typically through multiple cycles. Some products require as many as twenty prototype cycles in this phase. Others may only require two or three.

Phase 5: Design Finalization / Optimization

With all assumptions tested and validated, the design can be finalized and then optimized for production. To properly optimize for production, product design and engineering teams take into account the target production volumes, as well as the requirements of the manufacturer. Regulatory work may start in this phase.

Phase 6: Manufacturing Start and Support

Before production starts, tooling is produced, and initial units are inspected. Final changes are negotiated with the manufacturer. Regulatory work also should wrap up in this phase.