The digital age is among us, and businesses have had to play catch up. Piled up with data, they’re left to look for solutions that are both quick and cost-effective, often missing out on the opportunities for growth that come with proper data analysis.
By understanding the challenges that massive quantities of data can bring, companies can shift their approach and find ways to automate tasks that save employee time and company money while gaining insight from in-depth, accurate analysis.
This article will look at the seven most common challenges businesses face when managing data and analytics and what can be done to accelerate business growth.
Collecting real-time useful data
The collection of data is unavoidable, especially as companies set up more customer retention strategies.
Tweaking metrics when configuring data collection and analysis, risk managers can opt for automated tools. Collecting, processing, and organizing data without the need for human interaction, analysts can spend more time providing extensive information to organizations that can help them with lead generation.
When referring to the use of software geared toward increasing company presence online and converting leads, cofounder of EnterPH RJ Ledesma states, “We’ve since taken the business from a few clients to over 100 clients and continue to grow each year.” While organizations may get by, maintaining focus on one subset of data is not effective and can impede growth.
Managing influxes of data
Each company that takes the plunge to go digital has to deal with data. The larger they grow, the more data comes in, creating a mess if not properly organized. The influx of data creates challenges and logistical faults in business growth.
Organizations need to look toward scalable options such as cloud servers that provide automatability to free up risk management and data analysts that can be accessed from any device, anywhere in the world. In addition, businesses need to adapt to the web’s extended reach, connecting companies with resources dependent on the location.
According to cloud computing expert Barbara Ericson of Cloud Defense, “It doesn’t matter if the computational resources for a company or business are located halfway around the world. If the company has access to those cloud servers, they should be able to complete all the same objectives they need without a significant delay.”
Making collected raw data readable
Raw data collected online doesn’t really say much. It’s confusing in its original form and takes decoding or translating to turn it into readable data. Besides the collection challenge, companies find it increasingly difficult and time-consuming to turn data into visuals that speak. Without proper software, analysts must build their own graphs, using complex formulas to achieve their desired results.
When organizations learn how to integrate creativity with the power of big data, they can use visuals to increase performance, useability, and customer conversions. For the effective creation of visuals that paint a clear picture, organizations can invest in data systems that convert raw data to striking visuals in seconds.
Managing data coming from multiple sources
The web is full of data, all mingling, and intertwining. While it’s a mind-blowing concept to grasp, it means trouble for businesses and can stunt growth. The inaccurate analysis comes from the inability to filter data from multiple sources into a single location. If data analysts are left manually sorting through data from around the web, they have less time to analyze and understand the bigger picture.
Closing up multi-lane web traffic and designing a centralized system that collects and manages generated B2B leads and other essential data, employers can take a more direct approach to collection, adding in all the pieces for a complete analysis. Decreasing the time it takes to sift through data and figuring out how it all fits is key to helping organizations reach their growth goals.
Becoming overloaded with data analysis
There is only a portion of an organization dedicated to processing data, and all of the pressure falls on them. Upper management and CFOs often don’t understand analyzing data and its concepts, expecting far more than their capabilities allow.
Cracking under pressure and bombarded by work that never seems to end can stagnate growth and create a lack of skill as companies scramble to find someone to help them manage their data.
By incorporating an analysis system that works to collect and analyze data without the need for constant human interaction, corporations can save time and money, all while boosting employee morale.
Lack of necessary analytical skills
In some cases, data analysts and engineers are expected to come with all the answers to their data-driven issues. However, upon receiving the job, they’re met with a long list of challenges, for most of which they’re unprepared. Companies ignore the need for training, unable to grasp the difficulty that their employers are facing.
Instead of letting the pressure fall on analysts, organizations should invest in programs and training that prepare their employees. By learning the newest and most up-to-date techniques, analysts will have the skills they need to give accurate answers to employers, helping them operate more efficiently and plan more accurately.
Cat and mouse chase
When it comes to choosing methods to deal with data, there is one last thing that organizations need to be mindful about – security. According to research, the annual cost of data breaches came in at over $1.5 trillion, an amount that could send companies out of business and jeopardize their reputation.
Sticking to legacy technologies to secure data may seem safe but, the older the technique, the more obsolete it becomes. Software and storage for big data already has issues keeping information secure, constantly playing catch up with hackers.
The number of users with admin access and mean-time-to-identify (MTTI) are among the most important metrics to use for monitoring changes to your website’s database, helping security teams put a stop to attacks before they have a chance to start.
For organizations that collect customer data for record, securing their system and data storage locations needs a definite upgrade. Keeping up with the sophisticated attacks lurking around the web takes an aggressive approach that is able to monitor real-time activity and provide automated means of mitigation.
Conclusion
Dealing with data is a new challenge on the business timeline, throwing many companies for a loop. Now that there’s more knowledge of the powers of collected data, there’s been a shift toward finding new and improved methods to collect, organize, and store it.
Recognizing issues that prevent businesses from growing and reaching their goals can put companies in the right mindset to make changes in the way they look at data, giving it the reputation it deserves.
