Data analytics is the basis for all business decisions today because organizations realize the importance of a data-driven approach. The trial and error method is extinct when it comes to getting the right insights for B2B or B2C companies. Instead, artificial intelligence and machine learning models have all but guaranteed the availability of proven insights for companies.
But smaller businesses and those in third world nations are still relatively unfamiliar with the idea of data analytics. So let’s explore the fundamentals of this concept. In addition, we’ll discuss some of the key challenges organizations face in implementing analytical decision making.
The Era of Data Analytics
Data analytics has been around for longer than most people might imagine. It all started back in the 1960s when data warehousing, Big Data, and Cloud computing took off. Since then, data analytics has rapidly evolved thanks to advancements in computing, software, hardware, and data management processes. Today, an astounding 53% of companies worldwide use big data analytics, with more joining every day.
This technique is so widely used because it helps businesses generate higher revenues and understand consumers better. The digitization of consumer-business interactions generates immense data. For the first time in history, this data is not only easily accessible but easily analyzable as well. With advanced systems and automation capabilities, businesses can make sense of consumer behavior better than ever before.
That’s why companies and stakeholders are more prosperous now than at any time in the past. It’s no coincidence that organizations are breaching the trillion-dollar mark for the first time during the data analytics age. Yet, for all its benefits, data analytics is challenging to implement.
One reason is that its execution is complicated. Another reason is that it’s easier to get things wrong than it is to get them right. But let’s take an in-depth look at the central data analytics challenges facing companies today.
Challenges in Data Analytics
We’ve compiled an extensive list of challenges you are likely to encounter when implementing data analytics. So let’s dive in.
1. Aggregating Various Data Sources
There’s plenty of data sources out there. Whether it’s emails, social networking platforms, e-commerce stores, or simple search engine queries, there’s no consumer data shortage. And these disparate sources of data pose a challenge to those trying to aggregate them into a single analyzable system.
The first real challenge is gaining access to an analytics platform that analyzes simultaneous data clusters. The union of AI and data analytics solves this problem to a certain degree. As AI becomes more sophisticated, analytics platforms can analyze more massive amounts of data.
But integrating different data sources to a single platform requires the highest expertise. It takes considerable time to learn the skills necessary to pull off intricate data integrations. Besides, not every business has the resources to hire seasoned data scientists. Yet, most companies cannot afford not to integrate and aggregate disparate data sources.
2. Poor Quality Data
Another major challenge in interpreting data analytics is the quality of data. ‘Garbage in, garbage out‘ perfectly describes this predicament. If data sources are polluted, then decision making is compromised. That’s why data analysts spend so much time modifying data sets to make them useful. But data analysts aren’t meant for these tasks. Cleaning data sets and making sense of them is tedious and time-consuming.
Instead, organizations need to rely on automated data synchronization systems. This approach will free up more time for data analysts to do what they’re supposed to be doing. And automating data through AI means less room for human errors.
3. Lack of Organizational Autonomy
Despite being imperative to maintaining a competitive advantage, businesses are still having a hard time embracing data analytics. The most common manifestation of this reluctance is a lack of autonomy within the organization for data analysis. Firms that dedicate resources to analyze data outstrip those who don’t respect the discipline.
Unfortunately, senior management’s lack of support means data analysis departments struggle to churn out valuable insights. This lack of support can manifest in many ways, like an unwillingness to divert corporate funds to upgrading technology. Or it may be a sluggish effort to remove obstacles for data analytics experts. However, the lack of organizational support manifests, you can be sure it hinders a firm’s ability to stay relevant.
4. Scalability Issues
Scalability is becoming a challenge for organizations that are veterans in data analytics. Users generate more and more data each day, which means firms are running out of space to store it. Organizations need updated systems to keep up with their data analytics needs. Yet, such systems that ensure scalability are a significant expense. The real issue with data scalability is inefficiency in adjusting data levels to analyze.
Organizations require systems that can cope with the changing demand of data utilization. This feature allows a focused understanding of data without changing machine learning models. However, scaling data analytics requires a sound strategy that centers on business objectives. If firms lack a data analytics strategy, maintaining scaling techniques to cope with changing demands becomes virtually impossible.
5. Big Data Management
Managing big data isn’t for everyone. If it were, we would see more billion-dollar firms. The challenge in big data management starts with accumulating relevant data and then rendering it usable. Most organizations lack the time, resources, and skilled personnel needed to manage increasing volumes of data.
Especially worrying is that there aren’t enough data scientists to cope with the immense amount of data production. Data analysts require years of dedicated training to reach a level where they can provide this management expertise. But the marketplace doesn’t slow down for anyone, making it extremely difficult for companies to train data analysts. All of these challenges contribute to mismanagement of big data and, eventually, flawed decision making.
6. Data Privacy Issues
Data privacy goes hand in hand with data handling. Users and companies are now more sensitive to data privacy rights than ever before. Personal data breaches are the leading challenge by almost half of all data-driven companies. That’s because all manners of data are online now. In the past, some data was stored offline. But with the rise of cloud technology, that’s no longer the case.
Besides, accusations of data sharing and breaches in firms like Facebook and Google do not inspire users’ confidence. That’s why if firms are going to adopt data analytics, they need iron-clad security measures. Providing data privacy to gain access to user data is challenging in the current climate. But without that access, there’s no way to take advantage of data analytics tools and techniques to remain competitive.
7. Budgetary Constraints
Employing advanced data analytics techniques like predictive analytics isn’t cheap, especially when such procedures allow deep consumer insights. The cost of data analysts and analytical tools is rising as AI becomes increasingly efficient. Thus, companies are struggling to keep funds available for acquiring or updating data analytics resources.
Another reason for this budgetary constraint is a lack of tangible results. If firms lack proper KPIs for measurement, it’s hard to pinpoint how effective data analytics is. This makes senior management skeptical of its effectiveness, and thus it’s deservedness for funds.
Updating analytical tools isn’t cheap, either. A firm might be gaining significant results from data analysis yet fail to acquire the funding to update its tools. This brings us back to the scalability issue discussed above. So companies need to figure out a way to keep funds flowing for continued insights from data analysis.
8. Lack of Data Management Processes
Then there’s the challenge of lacking tech-savvy processes altogether. This challenge is more prevalent in old fashioned firms, those that began operation before the digital age. That’s because it’s harder to shift existing infrastructure to suit evolving business needs. With organizations relying more heavily on AI to understand and navigate consumer needs, technologically supportive management processes are more critical than ever before.
Big data is making the world of data analytics more complicated by the day. So companies need sophisticated models and management processes to cope with data analytics needs. For organizations that are not yet ready to do so, it’ll take significant time and resources to make the shift. The real challenge will be the shift in the culture of the organization. As long as people’s mentality within firms is technology-averse, so too will be the management processes.
9. Data Warehousing & Storage Issues
Finally, the challenge of data warehousing and storage capacity plagues firms. This challenge is rapidly becoming the main worry for data-driven organizations because of increasing data production. It’s estimated that users are generating over 2.5 quintillion bytes of data every day!
This makes it difficult for firms to accumulate data continuously. Advancements in data warehousing techniques are likely to solve this problem. However, are these advancements fast enough to keep up with the increasing levels of data generation? Only time will tell.
Conclusion
Data analytics is a double-edged sword. There’s plenty of benefits to it, but challenges are increasing rapidly. There’s no reason for firms to withdraw from advanced analytics techniques due to these challenges. But firms need to rapidly adapt their approach to overcoming these challenges to make the most data analytics benefits.
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