Top Three Data-Hungry Departments in Your Company
Data-driven is more than a buzzword in today’s business landscape. It is an approach that has transformed many business processes and workflows, creating a more efficient business operation as a whole. Data is used to make critical decisions, understand clients and customers, and find ways to optimize business flows like never before.
The use of data is further enhanced by new technologies like efficient data lakes and artificial intelligence (AI) that performs data processing. The introduction of efficient data storage solutions has allowed businesses to gather more data and significantly reduce the data collection costs. AI, on the other hand, markedly reduces the time needed to process unstructured data and generate insights. AI also automates the process of generating insights from a large amount of data.
In business, almost all functions can benefit from data and insights. There is more than one way to refine business processes using data. In this article, however, we are going to focus on the top-three data-hungry departments in your company and how they use data to perform better. Let’s have a look, shall we?
Marketing
We really cannot talk about data-driven operations without mentioning the use of data for marketing purposes. Marketing departments are among the earliest adopters of data utilization. Before big data and AI became popular, marketing teams relied on insights about their customers and the market to optimize campaigns and other marketing activities.
Data-driven marketing is now a common practice. Almost all marketing departments collect analytics about their campaigns, the users they engage, and communications in general. The latter often means following trends on social media and keeping up with market shifts through the recognition of patterns and market behavior.
Another way marketing uses data is through automation. Automation scales marketing teams exponentially: you can run more campaigns and reach more users by automating a lot of marketing functions and tasks. Automation relies on streams of data to work effectively. Simple data such as target users and personalization options allow for many marketing tasks to be fully automated without eliminating personal touches.
Data scraping is also a common practice. Instead of manually gathering contact information and the personal details of potential customers, marketing teams now run web scraping projects to collect thousands – if not millions – of personal details within hours. By tapping into publicly available data using means such as bots and a VPN service, marketing teams can generate leads faster. More about it on Smartproxy blog.
Human Resources
Another department that is very data-hungry is the human resources department. HR relies on data throughout its processes to monitor performance and enable better, more targeted recruitment. With the talent market being as competitive as it is today, data becomes a crucial competitive advantage for every HR department.
Hiring is a good example of how data and data processing can transform internal workflows. In the old days, HR specialists posted job vacancy ads and manually screened CVs in order to find suitable candidates. This is a time-consuming process and it is clearly not the most efficient way to find top talent. Thanks to social networking platforms, the process can now be simplified.
Web crawlers can automatically scrape the data of potential candidates from LinkedIn and other professional networking sites. Recruitment officers can learn more about potential candidates, their personal views, and even their past performance by tapping into candidates’ social media posts and online activities.
Even job ads can be made highly targeted. Social advertising networks like Facebook and LinkedIn offer data-driven targeting tools that any business can use. You can, for example, find a potential candidate for VP of Marketing by specifically targeting people with the relevant education and job experience.
IT
Of course, the best use of data is in the IT department. Everything from IT infrastructure and internal networks to front-facing channels and websites now turns to data for various purposes. There are some advanced cases of data use for IT purposes too.
For example, threat detection now relies heavily on data. Instead of dealing with threats when they appear, IT teams can build prevention models based on data about past attacks and security risks. Known attack vectors are mapped to the last detail using a large volume of data from previous attacks. Security measures can be deployed according to the insights generated by data processing.
The same is true with network utilization. Instead of manual checks, network administrators now receive a steady stream of data from nodes and network devices. Problems such as bottlenecks and network errors are identified almost immediately; in some cases, errors and bottlenecks can be prevented with the help of data related to usage patterns and the flow of information.
These are just a few examples. There are countless other scenarios of data use, boosting the value of data for businesses and expanding the possibilities of data-driven processes in the workplace to a whole new level.
