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Top 3 Data Analytics Challenges and How to Resolve Them

In this special guest feature, Jerry DiMaso, CEO and co-founder of Knarr Analytics, discusses how effective analytics has become such a determinative factor that it’s now evident that those who master it will thrive. However, the journey toward that goal isn’t without obstacles. What are the most common data analytics challenges and how can companies confidently confront them? Knarr Analytics’s collaborative cloud-based data analytics tool helps companies visualize, annotate, and share data in real time. Jerry is a passionate leader and author in the analytics space who has spent the past decade developing applications, advising on data and analytics strategies, and building analytics products. His work in more than 100 organizations across various industries has inspired him to take on the mission of enabling analysts to solve business problems faster and more collaboratively.

In 2013, McKinsey’s director Tim McGuire said: “Analytics will define the difference between the losers and winners going forward,” and he certainly wasn’t wrong. Since then, we have seen companies across the board leverage data capabilities to execute smarter decisions, drive accountability, improve their financial health, and keep a closer eye on their performance.

According to a recent study, the data analytics market is expected to grow at an annual growth rate of 30% until 2023, reaching $77.6 billion in annual spend. Such numbers show just how important data capabilities have become, hinting at a future where embracing digital business without data will be simply impossible. 

Effective analytics has become such a determinative factor that it’s now evident that those who master it will thrive. However, the journey toward that goal isn’t without obstacles. What are the most common data analytics challenges and how can companies confidently confront them?

Navigating budget limitations

Data analytics leaders need to act in the present but always think about the future. This means delivering business outcomes from data-driven programs while also building an effective data structure for tomorrow. Balancing these needs requires them to take ownership in developing a clear and comprehensive strategy. 

However, businesses often lack the right data and analytics organizational structure to lean on. In many companies, the data and analytics group falls under IT and is given very little in terms of headcount and budget, as they are typically seen as cost centers, which makes it difficult to justify high spend on analytics tools and skills.

To navigate budget limitations, managers in these groups should first understand the specific organizational needs to build their unique data management strategy. This way, they avoid investing millions of dollars into complex data management infrastructure only to find out they need much less than that. Moreover, analytics leaders can secure a budget for tools and skillsets by measuring the ROI of a system and highlighting both short-term and long-term benefits. In many cases, this should be a no-brainer: According to Forrester, insight-driven businesses grow at an average of more than 30% annually. 

Scaling data analytics

As organizations, and the amount of data they collect, grow over time, data analytics may become increasingly difficult to navigate. Without a strategy in place, the process of collecting information, collaborating on projects, and generating reports can easily go awry. To avoid inaccuracies, it’s key to implement a system that can grow with the organization and adapt to the rapid pace of change.

According to an IDC study, the success of big data and analytics can be driven by increased collaboration, particularly among IT, line-of-business, and analytics groups. That’s why risk managers should look toward flexible tools that offer a 360º view of data and leverage integrated processing and analysis capabilities. Automated collection and sorting, easy sharing and extraction, real-time collaboration, and the ability to condense diverse data sets into a single type of analysis are some of the most important factors to consider.

Poor quality of data 

Data is the lifeblood of an organization – so if it’s not of high quality, decisions will invariably be negatively affected. Having access to vast amounts of data that come from different sources – often with different formats and quality – is one of the most common challenges when it comes to streamlining analytics. 

It starts with the input: garbage in, garbage out, as they say. Companies risk making uninformed business decisions, reaching out with incorrect customer communication, and not meeting regulatory standards. Nothing is more harmful to your analytics infrastructure and team than inaccurate data, which is probably why the overwhelming majority of analysts’ time is spent on integrating and harmonizing the datasets to make them useful. 

This approach is highly inefficient and duplicative. To tackle data quality issues, companies should instead invest in centralized systems and data cleaning automation. These tools allow data to be input automatically with quality checks on fields, leaving little room for human error. And by leveraging easy system integrations, companies ensure that a sudden change in one area doesn’t bring any disruptions. 

Out of all the business trends we have seen in recent years, one has been perhaps the most pronounced: To stay relevant and competitive, organizations must embrace digital business. Data analytics plays a key role in that – but only when businesses invest in such data architecture that meets their needs, allows for scaling and collaboration, and makes data sorting and management highly efficient.

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