What is Data Management
Data management is the collection, retention, and use of data in secure, efficient, and cost-effective method. The goal is to allow people, companies, and organizations to use data to decide and act in ways which benefit the organization most. Data management is key because of the rising importance of intangible assets.
Data is Capital
Data is capital. It’s a key factor for people and companies as they create both digital and physical services and goods. Car makers need financial capital to produce a new model. It also needs data to create the algorithms which power the autonomous features of the car.
Data’s importance requires both strong management practices and a robust management system
Oracle discusses The Rise of Data Capital.
Data management requires a wide range of policies, procedures, and practices. Your data management system should
→ Be accessible, current data stored across the cloud and on premises
→ Use data throughout its operations through a variety of apps, analytics, and algorithms
→ Protect the data for both privacy and security
→ Allow for proper archiving and destruction of data as required and needed
What is a Data Management Platform?
A data management platform is the suite of software tools which allow the collection and analysis of the voluminous data every organization has available to it. These tools are usually developed by the database vendor or third-parties. These solutions allow the data team to
→ Identify, diagnose, and resolve faults in the database infrastructure or system
→ Allocate memory and storage capabilities
→ Make changes to the database design without disrupting operations
→ Optimize the system to allow for faster queries.
Many popular cloud data platforms allow rapid, cost-effective scaling as needed by each organization.
Data Management Challenges
The faster pace of business and wider availability of data present many challenges. Organizations therefore need to locate the most effective tools. This selection process needs to address several key factors.
→ What data do we have? Given all the available sources -- sensors, social media, cameras, and more -- lots of data is being stored. If the content, location, and potential uses of the data are unknown, then...it’s no good.
→ How do we maintain our data performance levels? Given the amount of data available in storage, the access of that data needs to keep up with the demand for it.
→ How do we remain compliant with laws and regulations? The laws and regulations regarding data are complex. Multiple jurisdictions impose requirements. The requirements change rapidly. Organizations need to protect personally identifiable information especially, because of the increase in strict regulation.
→ How do we use current data in new ways? Processing data and using it as circumstances change is crucial, and the platforms need to be allow this flexibility -- especially in the era of COVID-19. If it takes time and effort to convert the data into something useful, that’s time and effort which could be spent better elsewhere.
→ How can we keep up with data storage changes? Betamax is no longer. Data is stored in multiple systems, and data needs to be accessible no matter the changes in technology. The data also needs to be transformable into any needed format, model, or shape for effective use.
Best Practices for Data Management
Data science informs the best practices for the management of your data. Data science uses a wide range of methods, processes, algorithms, and systems to make the data useful. Statistics, computer science, and business all create the tools allowing the analysis of data obtained from all channels -- the web, customers, sensors, smartphones, and more.
In a data science environment, the organization makes sure that it both knows what it has and how to make it useful.
→ The discovery layer, which rides on top of the data, allows your team to find the datasets which make the data usable.
→ The data science environment allows the repurposing of data. It automatics the data transformation, and makes sure that models can be tested quickly and are run efficiently.
→ Autonomous technology makes sure that performance levels remain high all across the environment.
→ Discovery tools make sure you remain consistent with compliance requirements for all the jurisdictions you operate in.
→ A common query layer ensures that the data can be accessed easily by everyone who tries to access it -- and that it doesn’t need to be manually transformed.
Data Management Evolves
Data is capital. Data makes sure that everyone knows what startups and disruptors already know -- data is central to your ability to make decisions, take action first, and identify coming opportunities. Data’s rising importance means every business needs to derive high value from this capital asset.