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Data Collection & Data Strategy for Your Business

Take a moment to comprehend the staggering magnitude of the reported daily data generation of 2.5 quintillion bytes, then exhale knowing that we have you and your data strategy gathering plan covered.

An increasing amount of data is being produced. The total amount of data was 64.2 zettabytes in 2020, and Statista projects that it will increase to 181 zettabytes by 2025. If you don’t know where to begin, this wealth of information may be too much to handle.

How do you make sure the data you use is pertinent to the business issues you’re trying to solve? A data-driven choice is only as solid as the data upon which it is based. Self-data collection is one approach. By gathering data, you can make sure that information is accurate, comprehensive, and relevant to your organization and the current problem.

Here is a list of the many sorts of data, what you should know before you start collecting, and seven different ways to collect data.

Data strategies- what are they?

A data strategy is the first step in creating a culture that is more data-driven. The basis for all your data and analytics solutions is a data strategy. It’s not a band-aid fix for your data issues.

It is a long-term, strategic plan that specifies the personnel, operational practises, and technical infrastructure that must be put in place to solve data concerns and accomplish corporate goals.

Although a data strategy is frequently think of as a technical exercise, a contemporary and thorough data strategy tackles more than just the data; it is a plan that defines people, process, and technology.

The Seven Components of a Data Strategy

Let’s discuss how to begin developing your own data strategy. Numerous businesses with various technological requirements and analytical maturity levels have sought our assistance in developing their data strategies.

The following components were discover from this experience and can be use by any business that wants to advance with its data.

You might be interested in: https://www.indiumsoftware.com/blog/well-implemented-data-analytics-strategy/ 

Business Requirements

For data to meet strategic objectives and produce true value, it must specifically address business demands. The selection of a champion, all stakeholders, and SMEs inside the organization is the first stage in the definition of the business requirements.

The executive leader who will garner support for the investment is the data strategy’s champion. Stakeholders and other SMEs will speak for corporate divisions or functions.

The strategic goals will then be establish, and department operations will be linked to organisational goals. It’s normal for goals to exist at the organizational and departmental levels, but they should be align. The most efficient way to achieve these goals is through an interview process that begins with executives and moves down to department heads.

In the end, we will identify the KPIs to address those questions after learning what leaders are trying to measure, what they are trying to improve, and questions they want addressed.

We get beyond the initial obstacle to many IT or technical projects—not knowing what the business is trying to achieve—by first collecting and documenting the business requirements.

Data Sourcing and Gathering

We can move on to the next step, which is examining the data sources, how the data is collected, and where the data is located, if we have a solid knowledge of the questions the business is posing. It’s doubtful that all of the data will be on hand and in an accessible location within the company. So, in order to identify the source, we must go backward.

When data is available internally, we identify the source system and any access restrictions. In order to successfully respond to the question, we must also verify whether the data is accurate and updated at the appropriate intervals. We’ll make a note of any unavailable data here and pick it up in the following phase.

For instance, a retail business would be interested in learning how a brand is recognized before and after a significant product launch.

Let’s assume that the retailer has information about its call centers, statistics on online and in-store traffic, and total sales and return figures. That does not reflect consumer sentiment or what consumers are saying about the brand.

The shop may decide to obtain sentiment analysis via social media. In this step, sales, traffic, and call center data would all be record, and there would also be a chance to bring in social media data.

To properly respond to the question, the information must be accurate and updated often.

Data Transformation for Insights

Data visualization is important, and a data strategy should offer suggestions on how to use analytics to derive important business insights. Many businesses still use Excel, email, or an antiquated BI technology that prevents data engagement. When relying on IT to produce reports, a bottleneck is creating since laborious manual processes are frequently necessary.

In addition to creating the data to be visually appropriate, data visualization technologies should also make the data simpler to comprehend and interpret. When selecting a data visualization tool, the following elements should be considering:

  • Visualizations: You should be able to recognize trends and outliers quickly and keep confusion from being created by poor presentation.
  • Storytelling: The dashboard ought to outline the context of the measurements and foresee the user’s course of research and diagnosis.
  • Data Democratization: Who has access to what information? Promote adoption and sharing and establish uniform definitions and measurements across the entire organization.
  • Data granularity: Ability to deliver the appropriate level of detail to the appropriate audience. An analyst might require more in-depth data than an executive, and certain people might require drill-down options.

Data Governance

Data governance is the grease that keeps the gears of an analytics practise turning and what finally permits enterprise level data sharing.

A programme for data governance will make sure that:

  • The information from across the company is use to determine calculations that are use throughout.
  • The proper individuals have access to the proper data.
  • A Data lineage is describe as the history of the data, including its genesis and subsequent transformations.
  • Data governance is a problem that needs to be solve by people, not by a tool. Data governance requires initiative and, occasionally, the ability to navigate challenging conversations.

Processes and People

As we’ve said, moving toward a data-driven culture calls for more than simply technology. In this phase, we examine the internal personnel and data creation, sharing, and management procedures. More data, data analysis, and possibly new tools will all be including in a data strategy.

Likewise, in light of this, it seems sense to consider the users’ skill sets in order to identify their areas of strength and support needs.

Do they require training in data and analytics? Do you need to bring in more staff? It’s important to evaluate the organizational structure as well. Should analysts report to IT or a business unit? And how can IT assist the company in meeting its analytics needs? It is important to examine everything, including incentive programmes and employee reviews.

After all, these tools can use to motivate staff to use data in the manner that the company desires. When workers are given new tools without being taught a new attitude, the result is unlikely to change.

Here is an illustration of a training programme we developed for a client that involves employees at all organizational levels.

Conclusion:

Many businesses have inadvertent process barriers that prevent them from using their data for decision-making. Data analysis may require reengineering of business processes. Documenting a process’ phases and the instances when particular reports are use to inform decisions can help with this. Additionally, we can require that specific data be use to support a business decision.

In order to create internal momentum and foster positive data-related behaviors, it is important to recognize and publicize wins that are based on novel uses of data.

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