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Kick Starting Data Science Project with Requirement Gathering and Data Collection

It is difficult to collect requirements in a data science project as a business usually never does anything like it and might not be able to completely describe what they want to do. Data science is continuously growing over time, and through interaction, facilitation and negotiation between business functions, IT and other key actors it is important to add value to decision-making.

Nothing new is to find business insights through information. Organizations usually track various metrics for income, competitiveness and risk, or help sales and marketing, etc. operational efficiencies. For these situations, the specification engineering aspects typically include the establishment of key steps and the identification of useful reporting formats to provide companies with timely diagnosis and indication for potential actions. But the digital data produced currently doubles every two years, reaching 50 billion gigabytes by 2022 , according to an International Data Corporation analysis. This large quantity of data produced by the digital age customers and company outweighs prevailing data storage technologies.

The criteria for the collection of data and processes are a critical part of effective project management and implementation in the start of a data science project. While the importance of requirements selection for application development is acknowledged, insufficient has been done to clarify the underlying need for a data science method. This was also usual to blame the victim: in other words, the client was to blame for the lack of clarification about the company requirements. Customers / Users may be driven through a process that meets their business demands and enables the precise selection of specifications, but analysts must consider both the principles of business needs and the method that best documented them.

Budgets and resources are limited, time constraints are relentless, and the organization constantly requests updates, upgrades and new services. Not surprisingly some businesses are still not giving time to evaluate data and processes, despite the misperception that short-term growth efforts and cost reductions would be high. The final results are, however, quite the opposite.

The structured framework for executing projects in data science used by Whiz IT services is focused on requirements collection and documentation.
The analysis of data and process specifications is a skill of an analyst:

    1. Interviewing subject matter experts and relating needs
    2. Organizing complex information into understandable subject areas
    3. “Translating” technical language into business language and vice versa
    4. Ensuring stakeholder involvement at all levels of req
    5. Drafting clear and concise written documentation for users and technicians
    6. Working successfully with multidisciplinary teams

This approach captures user specifications easily, reliably and fully–an approach that guarantees a high-standard quality specification that is versatile yet standardized at all times. In order to make sure the data science project is a success, we have to meet extensive business criteria to gather initiatives.

Because the analysis of business requirements is equally essential for data collection. If the problem has been identified, you would need data to provide you with ideas to resolve the issue. That part of the process includes talking about what information you need and seeking ways to access that information if it includes querying existing repositories or obtaining outside datasets.

Data collection from an organization can be done in well directed manner and this data can be differentiated in 2 parts

  1. Internal Data

Internal data lets you run and organize your business. Internal data will help businesses who want to boost performance and profitability and those which do not make a profit. However, internal analysis uses data from inside an organization to help make decisions about important topics. Four types of internal data will provide the information required for applying new approaches to company owners and members.

    1. Sales Data : Many business functions are more malignant than sales, and one explanation is that it is critical for competitiveness of an organization. Revenue statistics that include revenue, competitiveness, delivery networks, costs, consumers and the differences between what is generated and what is purchased by consumers. Sales results can help business owners identify success points and vulnerability points that can lead to a shift in strategy or emphasis.
    2. Financial Data : The financial department of a corporation may produce useful information such as reports on growth, cash-flow reports and budgets. The precise sums spent on manufacturing goods and services are explained in the production records. Cash-flow accounts describe how much capital was spent over a given amount of time within the company. Budgets contain information about how the funds have been spent and what has been allocated. It is unlikely that businesses will survive and tend to blow up their budgets without substantially that revenue. When compared to sales, which offer statistics about the amount of goods or services provided, financial reporting shows the expense and complexity of the expenses of the businesses to produce such items and services.
    3. Marketing Data : The marketing department of a company focuses on the advertising of goods and services, brand recognition and client and expectations. Departments in marketing are electronic treasure chests. They can build consumers monitoring, customer accounts, amount of social media ads, brand recognition, competitiveness level and level of engagement through website and material. They can also produce knowledge about customer actions. Analyzing internal marketing data that help business owners determine what marketing campaigns will succeed, which marketing campaigns may require changes and what new campaigns will be successful on the basis of customers ‘ desires.
    4. Human Resources Data : Without a committed and productive workforce, companies can not thrive. Human resources may provide statistics on the cost of hiring and training a staff, how the efficiency of the actual staff is affected by absenteeism and whether workers are happy or unhappy with the organization. When customers are depressed, unproductive and unmotivated, a company can not succeed. Personal data will show the places that an organization wants to change the operations to motivate and maximize the employee’s capacity, expertise and sweat equity.

Internal data lets you run and organize your business. Internal data will help businesses who want to boost performance and profitability and those which do not make a profit. However, internal analysis uses data from inside an organization to help make decisions about important topics. Four types of internal data will provide the information required for applying new approaches to company owners and members.

Information collection for your own company does not take place without political and ethical considerations. When a person in one part of the organisation, or the entire business, looks for data on another, the stability and safety of the people can be affected. It can be complex and sometimes difficult to find data. Such data are fairly easy and can be reported on time or an external factor dictates the compilation and publishing of the data. The reasons that a company is so closely tied to its culture are generally explained by its competitive environment.

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  2. External Data

    External data will make a difference when it comes to making decisions about the business ‘ future, learning more about the health of a market, deciding which new products are launched and when they are launched and several other fields of operation. Publicly available data such as census, electoral statistics, tax records and internet searches or Private data from third parties such as Amazon, Facebook, Google, Walmart and credit reporting agencies like Experian can be termed as External Data.

    External providers make high-quality information and data accessible for the reuse for strategic planning by organisations, vast volumes of data are freely available on website providers. High-level peer organization data makes it possible to make comparisons and time series and historical data allow over time comparisons.

External data will make a difference when it comes to making decisions about the business ‘ future, learning more about the health of a market, deciding which new products are launched and when they are launched and several other fields of operation. Publicly available data such as census, electoral statistics, tax records and internet searches or Private data from third parties such as Amazon, Facebook, Google, Walmart and credit reporting agencies like Experian can be termed as External Data.

External providers make high-quality information and data accessible for the reuse for strategic planning by organisations, vast volumes of data are freely available on website providers. High-level peer organization data makes it possible to make comparisons and time series and historical data allow over time comparisons.

With internal data Whiz IT always opt for external data from various sources because:

    1. Cost-Effective Alternative : Data collection will take time and resources, so the cost-effective solution is external data. Secondary data readily accessible can be used for the same reason. O However, others have also gathered these data for distribution, making them more affordable and less time intensive. It is much more important for market analysts due to the quality of secondary data about time, expense and energy.
    2. Time-Saving Accessibility : The usability of secondary data is another important thing. It takes nothing more than a couple of Google searches to locate a reliable source of accurate facts, such as a government department or market leader. Public documents can also be accessed free of charge in libraries and online. Find sites who have made good use of their work when carrying out your own study or as a supportive viewpoint.
    3. Credibility-Enhancing Perspective : While secondary sources are quick and simple, they also provide the analysis with insight and credibility. Secondary data is particularly useful in the gathering of information as it promotes previously discovered evidence while encouraging analysis into new problems and viewpoints. You build an extra trust and reputation layer with the reinforcement of the current data with new perspectives. An objective source suggests that many individuals got the same findings, decreasing the probability of error. Therefore, secondary analysis will also verify or expose inconsistencies in the current records.
    4. Resource for Primary Research Design : The use of additional secondary sources not only adds to the depth, but also makes the evidence simpler and more informative. This will help you devise new questions or methods of study for supplementary knowledge by primary analysis. Additional awareness encloses the core material, exposing a focal point.

Sometimes, companies differentiate data with them as primary data and secondary data while primary data comes within the organization the secondary data comes from websites like google, social media. Though it comes from a company account, it is gathered by a secondary partner hence considered as secondary data.

Whiz IT’s Data scientists are well versed in data collection be it internal data for data science project or external data. They know how to handle interdepartmental collisions well and gather necessary data from the data available within the business. Also they are well trained to understand business requirements and collect and analyse proper external data for business growth.