the science of data acquisition

The Science of Data Acquisition I

For any project, either intending or ongoing, data acquisition and data quality are vital to the final output. Notably, anyone can acquire data but highlighting the suitable parameters for acquisition, their consequent indicators, and the methodology that guarantees maximum precision remains the distinguishing factor in the context of the science of data acquisition.

More so, error-free data sets the tone for detailed final information. Thus, much emphasis has to be on accuracy in the data acquisition phase.

At the kernel of the project strategy, is the decision about the data set to be used and the acquisition methods to be explored. For a project that intends to generate primary data through field processes or questionnaires, a clearly defined routine of acquisition, and acquisition channel has to be designed. On the other hand, projects that source secondary data through the review of existing records and research are required to define the approach of utilization to maintain homogeneity of results. 

What is Data Acquisition?

Data acquisition describes the process of sampling real-world physical conditions or existing data to extract vital parameters. These parameters can be read, queried, and analyzed using certain applications on a computer or any other digital system.

The ‘Where’ in Data acquisition

When data is collected, the ‘where’ components of the data are an integral element of its usefulness. From time immemorial, the location component of items and persons has always been at the centre of crucial decisions. Although when acquiring data, the intent of the project drives the modus operandi, the location element enhances the comprehensive judgment of the data for individuals or groups that weren’t involved in the collection process. Consequently, when data that possess accurate location attributes are acquired, the ‘where’ in terms of coordinate references qualifies them as Spatial data.

Today, more attention is being given to geographic technologies as they help in acquiring data with the utmost confidence in its precision. It has been estimated that data acquisition typically consumes 60 to 80 per cent of the time and money spent on any given project. Therefore, care must be taken to ensure that data tasks remain mindful of their stated goals so that execution proceeds efficiently and effectively. 

What determines the components of a Data acquisition process?

Data needs:

The specific data needs determine the considerations for the acquisition process. Why are these data required? Of what purpose are they?

Cost:

For any project in the world, the budget is integral to the overall operation. In data acquisition, it is often cheaper to use secondary data than acquire primary data. Hence, projects with limited budgets tend to source secondary data that fit the bill. Large budgets give room for primary data sourcing with all intricacies and complexities considered to provide the information of interest.

Data type and currency:

For many kinds of work, the data need to be current, for some, data may need to cover a specified period. For others, data needs to be in a specific season. When all this sophistication is understood, the component of the data acquisition process can be finely decided.

Data sourcing and the questions

The huge volumes of data sets available today have spurred a lot of actions and questions about data collection. These questions include;

  1. What are the techniques for primary data collection?
  2. Are Data-acquisition As A Service(DaaS) companies the best channel to explore for a data acquisition project?
  3. What are the competent methods of secondary data sourcing?
  4. Are Data-acquisition As A Service(DaaS) repositories able to eliminate the limitations of secondary data?
  5. What are the parameters to be considered to achieve efficient data acquisition?

The effort to provide great answers to these questions is consistently shaping the evolution of the data acquisition process.

What is Spatial Data?

In simple terms, spatial data is data about a location, structure, or other objects in physical space. It can include attributes such as the latitude and longitude of a form, and the size and shape of earth features. Notably, every element that occupies a position possesses a location reference. However, it is essential to note that the data acquisition needs broadly determine if the spatial component of a data will be of interest.

Also, spatial data can be acquired through primary or secondary sources depending on project factors.

Spatial Data acquisition and its onions

It is considered that about 80 per cent of all organizational information possesses a location element, either referenced by address or coordinates. Therefore, there is a growing demand for access to accurate geospatial data for decision-making processes at all levels. The need premises on the notion that better resourced; and informed communities can effectively address critical, social, environmental, and economic issues with accurate spatial data.

Spatial data collection describes the methods, processes, and actions combined across the board to collect multifaceted information while emphasising their ‘where’ element. It highlights the data collection project from the idea phase to the execution stage. Consequently, several channels can be explored when acquiring spatial data depending on the available instruments and expertise. The sources and methods used can affect the accuracy of the spatial database or repository and the validity of the recommendation developed after the acquisition process.

Spatial Data: The sources and Methods

Surveying:

Surveying is an age-long method for spatial data collection. It is a constantly evolving field that has seamlessly answered all questions poised regarding the spatial reference of any data. In recent times, it has become enhanced by the evolution of GIS and its technologies. Experts can now acquire data using mobile GIS applications while maintaining or improving the accuracy standard. Through geospatial software and applications, data can be acquired without the need for heavy machinery.

Remote Sensing:

Remote Sensing is another fascinating technology playing a crucial role in the spatial data collection field. The deployment of satellites, UAVs, and drones capable of acquiring high-resolution images have revolutionized the science of spatial data acquisition. Consequently, technologies such as hyperspectral and multispectral imaging have triggered a new level of data variety. Multiple scenes of image data can be acquired, opening the doors to efficiently acquire information regarding natural resources, terrain information, and phenomena position.

Data Repositories:

Sourcing data is a dynamic act. When the needs of the spatial data gathering process are objectively listed, secondary sourcing could be the best fit. Data repositories are a network of third-party sources that allow businesses to draw data from their rich databases. Numerous government agencies like NASA, and DOD; private organizations like Planet labs, and Milsat Technologies provide a range of information relating to several themes that are relevant to study.

Data acquisition science and Data definitions

The spatial perspective of the science of data acquisition enables us to explore the bigger picture of data variety. However, acquiring data with specific strengths and needs requires the location element to attain maximum use across the board. Thus, without exploring the spatial component of data, optimal data definition and understanding of the science of data acquisition becomes uphill.