The Data Flood Challenges: How to embrace data lakes in large organizations?

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The Data Flood Challenges: How to embrace data lakes in large organizations?



Although processing and segmentation of data lakes are not simple processes, they are crucial to the functioning of an organization. A company needs the right analytical tools that will prevent it from data flood. So how to take advantage of all the data your company acquires or generates?

What does Big Data mean for an organization?

In 2001, Doug Laney published an article in which he presented a 3D formula describing characteristics of Big Data.

Among the characteristics of Big Data are:

  • Volume – Technological advances, especially widespread access to the Internet, have led to data pouring into organizations and companies in quantities never seen before. Moreover, modern solutions allow large amounts of data not only to be captured, but also to be collected and archived.
  • Velocity – This is the speed at which data can flow into an organization, but also the pace at which such data can be updated or become outdated. In addition, it can also be said in connection to velocity that Big Data are available almost immediately, unless they are also associated with a transaction or interaction with the organization. An example of this may be any user data that an e-commerce seller receives just after making a purchase in their store.
  • Variety – Big Data are not homogeneous, but may be both quantitative and qualitative. Therefore, the diversity of Big Data will require different, sometimes unrepeatable, analytical processes.

Big Data is not only about data from external sources such as purchased databases, purchase and sale transactions, social media, or data generated automatically by computers.

Big Data refers primarily to all of an organization’s data, about the organization itself:

  • Messages conveyed within the organization itself, between employees, such as the content of emails and in-house communications
  • Employee data
  • Data about the tasks employees perform
  • Data about revenues or losses

Why it matters? Importance of any data company owns

Good organization and focus on ongoing development can allow a company to take advantage of any data it owns and continues to accumulate.

The biggest benefits of company data are:

  • Indications of the causes of failure and errors in the business’ activities and development strategy
  • The ability to reach a user quickly, based on their behaviour, which can be deduced by collected data
  • Calculations of the risks and threat associated with a project, in just a few minutes
  • Identifying inappropriate customer or employee behaviour before it can have a significant effect on the organization

What to do with data in order to extract the most benefit?

Why does an organization collect and store any data? First and foremost, the goal is to draw as many results and insights as possible, to help prepare new business strategies that increase profits, optimize decision-making processes, set production, or reach the right user, with the right message at the right time.

When the company is already in possession of all sorts of data, this information needs to be organized and segmented. Only segregated data can be used to carry out correct analysis and draw appropriate conclusions that will be relevant to the functioning and development of the company.

And that is precisely where the problem usually begins. How can data processing be automated and optimized so that you can extract relevant insights from the information? And are there tools that will not only make data easily accessible, but will also allow such data to be applied in a manner useful for your organization?


Helpful and popular data management tools

The specificity of an organization’s activities and the purpose of the conclusions drawn from data analysis should be determine the type of tools used.

Data and communication

Communicators such as Slack, Google Hangouts, or simply corporate email boxes represent the first example of data tools. Their advanced features, such as creating theme groups, dedicated project mailing groups or dedicated internal newsletters, help you organize your communications and ensure that your information is available as quickly and as easily as possible.

Data and project management

Project management software such as Asana, JIRA and Nozbe are another example of data management tools. These programs help you to integrate many of the Big Data flow sources, such as Dropbox, calendar, address book and the content of correspondence with your customers. Using such a tool in a company makes it easy for all appropriate parties to access complete information about a project, which facilitates the communication process.

Data and managing customer relationships

Customer relationship management systems, or CRM, are of great importance to the operations of companies. These systems can perform many functions, from pure data collection to SFA implementation, trade promotion management and campaign management.

Data and event planning in an organization

Systems designed to streamline an organization’s performance and productivity are useful for internal organization processing. An example of such a tool is Eventory Planning, which allows you to manage remotely the delegation of employees to industry events and estimate the efficiency and profitability of such trips. Moreover, data collected by this tool help the organization to draw conclusions useful for the future, about whether the event was profitable from the business point of view.

Data and the effectiveness of marketing activities

Analytical systems which, based mainly on quantitative data, help an organization to draw conclusions about the effectiveness of the business, are a completely different type of tool for managing data. Such tools include Google Analytics and Facebook Analytics.

Of course, these are just some examples of tools useful for collecting data. One of the key areas in developing large databases is the continuous search for new ways of using data flows and finding ways to acquire and process new data. As a result, the market for tools for processing data is extremely open and is still growing.

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