Trend and impact of data analytic

How good data analytic is concluded?

With the advance technology, allowing exploratory data analysis (EDA), where new features in the data are discovered and follows by confirmatory data analysis (CDA), where existing hypotheses are proven true or false.  to the end, with Qualitative data analysis (QDA) is used in the social sciences to draw conclusions from non-numerical data like words, photographs or video.

What are the advantages of Data analytic?

The main benefits of data analysis are rather self-evident. How can someone improve their processes and identify problematic issues if they are not willing to look at the data? The answer, of course, is that they cannot make reliable improvements without data analysis. The key word here is “reliable!” Most people have a general idea about possible changes that “should” or “could” improve their processes. However, when it comes to these sorts of changes there is the inherent risk that the change does not have the desired result. There can also be unexpected consequences that impact some other aspect of that organization in a negative manner.

Having said that, the following are just some of the benefits of proper data analysis:

  • Allows for the identification of important (and often mission-critical) trends
  • Helps businesses identify performance problems that require some sort of action
  • Can be viewed in a visual manner, which leads to faster and better decisions
  • Better awareness regarding the habits of potential customers
  • It can provide a company with an edge over their competitors

Ultimately, there are six useful things you can do with Data analytic technology:

  • You can make an immediate decision.
  • You can plan in support of future decisions.
  • You can research, investigate, and analyze in support of future decisions.
  • You can monitor what’s going on, to see when it necessary to decide, plan, or investigate.
  • You can communicate, to help other people and organizations do these same things.
  • You can provide support, in technology or data gathering, for one of the other functions.

Technology vendors often cite similar taxonomies, claiming to have all the categories (as they conceive them) nicely represented, in slickly integrated fashion. They exaggerate. Most of these categories are in rapid flux, and the rest should be. In Data analytic technology still has a long way to go.

Disadvantage of Data analytic?

  1. It properly making worst in near term
  2. Requirement of integration – Digging into your requirements for business data may turn into easier avenues toward finding the right data. It may also expose a deeper problem with integration that befuddles data projects, big or small.Often, these tools require large technical teams; the hardest part is balancing the effectiveness of the technology with the capital and operational cost constraints
  3. Developing a single EDW (enterprise data warehouse) – in the process of combining and constructing one data warehouse for all your enterprise functions, big data may stomp out those plans for good .
  4. Existing infrastructure – belief that present information infrastructure is sufficient.
  5. Privacy concern – One of the lures over big data plans is the promise of finding profits in social media posts and public sources. Rumbling beneath that are quandaries among the legal community and at the Government level about business big data plans with personal information including information stored in the cloud.

Who studies and conduct data analytic?

Information technology, the term has a special meaning in the context of IT audits, when the controls for an organization’s information systems, operations and processes are examined before determine whether the systems in place effectively protect data, operate efficiently and succeed in accomplishing an organization’s overall goals.

Data analysts compile and analyze data for businesses in order to identify problems and suggest possible solutions. They might also design and build databases to house the information they need, ensure data accuracy and make recommendations to business managers about how to improve efficiency or quality based on their findings.

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