With the growth of business data in recent years, businesses are struggling to improve data management practices and are constantly in search of advanced collection and storage, management, governance and security platforms. This trend will bring a shift in culture across enterprises, leading to more data-focused business environments.
Much of this will be focused on structured data, highly organized information that can already be uploaded into databases indexed, and detected by search operations or algorithms – usually consisting of objective, numerical information that doesn’t need interpretation, such as transactional data, machine, and sensor data and mobile data.
Unstructured data, on the other hand, encapsulates all other data – that which resists easy indexing: often human-generated and language-based, it may not conform to database organizations. Some examples of unstructured data include Information contained in emails, audio and video files, blogs and wikis and postings on Twitter, Facebook, Instagram, and other social media platforms.
What is data management and how can it help in analyzing data?
An administrative process concerned with the acquirement, validation, storage, protection, and processing of data to ensure accessibility and reliability, data management is vital. Organizations are making use of Big Data more than ever to gain insights into customer behavior, inform business decisions and analyze trends.
Important points of data management are:
- Data Access: your ability to retrieve information. Certain technologies make data access as easy and efficient as possible
- Data Quality: the practice of ensuring that data is accurate and usable, starting from the moment it is accessed and continues through various integration points, to the point, it is reported or published.
- Data Integration: steps for combining different types of data, with data integration tools helping to design and automate these steps.
- Data Federation: a kind of virtual data integration which allows you to combine and view data from multiple sources without having to store the combined view in a new location.
- Data Governance: an ongoing set of decisions and rules for managing data to ensure that the business strategy and data strategy align.
- Master Data Management (MDM): managing, unifying and defining all common and essential data in an organization, with master data typically being managed from a single location.
- Data streaming: analyzing data as it moves by applying logic and recognizing patterns, filtering the data for multiple uses.
Data Analysis
Data analysis involves the methods and actions which are performed on gathered data to help detect patterns, describe facts, test hypotheses and develop explanations – this can include statistical data analysis, data quality assurance, modeling, and interpretation of results
DATA ANALYSIS TECHNIQUES AND TOOLS
Is data analysis well-defined or exploratory?
Data analysis can include testing for statistical differences, or looking for unknown relationships. Exploratory analysis requires graphical tools to assist with visualizing the relationships between variables and must be guided by the researcher.
Is the technique well-defined or experimental?
Most statistical packages can perform well-defined analysis, though custom applications or software packages are required for less well-known statistical comparisons or tests.
ANALYSIS MECHANICS
Will this analysis be repeated? How often?
The more analysis is repeated, the more important automatic the process becomes, especially if the analysis requires manual transformations be applied to the data. All experiments should be reproducible, by both the original researcher and others.
Will the analysis be repeated on different data?
Will data be provided from new devices or sites in the same format as the original experiment?
How many researchers will be accessing the data?
Documentation of the analysis becomes more important the more people are involved, and a tradeoff between the level of automation and level of training becomes clear as more people become involved in the analysis process.
How long does the analysis take?
Computational procedures may take anywhere from several hours to several days to complete.
DATA MANAGEMENT TRENDS
Increased Cloud Usage
Many businesses have recognized the benefits allowed by the Cloud, including the ability to streamline costs and processes, and Cloud solutions are seeing an expansion in business. More businesses are likely to opt for hybrid Cloud solutions in 2019 because it is safer and more convenient to keep some data on the premises.
More Attention to Data Security
Many of 2018’s high-profile data breaches have drawn attention to data security, and with so much on the line for customers and businesses alike, it is essential to protect sensitive data. Companies who gather and analyze data are looking to invest in cybersecurity solutions which minimize the risk of data breaches.
Stricter Privacy Regulations
GDPR and New Data Governance regulations have marked an era of stricter privacy regulations, and the norms for data management are set to become more stringent as security and privacy concerns grow more severe. Companies will need to work harder to ensure their compliance with new data management regulations.
Continued Use of Data Enhancement Solutions
One of the primary conditions for business success, quality of data is important, and companies will likely keep using Data Enhancement services and solutions to ensure they have enterprise quality data. Data enhancement solutions will also become more efficient and versatile as more businesses become interested in maintaining and improving data quality.
Aiming For More Value from Gathered Data
As data plays such a vital role, businesses will strive to make maximum use of the data they already gather, improving existing technologies such as predictive analysis with AI and machine learning becoming more feasible once they have a better grasp on their data management. Proper data management will become a priority goal for businesses in 2019.
Data Entry by an Expert Still Stays Relevant
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