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Organizations now have more data in play than ever before, to an unprecedented level. While this creates an ever-increasing amount of complexity for business and IT leaders, it also presents an opportunity to improve strategic decision-making for a range of business advantages.
Sophisticated data analytics tools and techniques can help mitigate the growing challenges that enormous volumes of data and metadata present. Organizations can now use intelligent technology to extract tremendous value from their data, enhancing their strategic decision-making as a result.
Despite these opportunities, many organizations are failing to harness their data to its full potential. In most cases, this is because achieving the required level of data analytics maturity requires a new approach to data entirely.
This article will explore how organizations can overcome the common pitfalls that may hinder them from advancing their data analytics maturity.
Breaking down the process of improving data analytics capabilities, it becomes clear that common challenges hold organizations back from progressing through the stages of analytics maturity, from descriptive analytics to diagnostic, predictive, and prescriptive. And these levels of maturity have now advanced even further to cognitive, self-learning analytics capabilities, thanks to artificial intelligence (AI) and machine learning (ML).
Perhaps the most crucial challenge here is that many organizations lack modernized processes for collecting, storing, managing, and analyzing data effectively. This may be due to outdated manual processes or lack of policies and best practices for keeping data consistent and up-to-date. Some organizations simply don’t know how to deal with the vast volumes of data or don’t have sufficient availability with new forms of data like social media, sentiment analysis, geotagging, and more.
With such vast volumes of both company and customer data across so many sources, trying to bring data assets together for analytics and decision-making purposes can be daunting. Some organizations have evolved to successfully embrace and navigate this new era of data, while others remain overwhelmed.
Winning senior leadership buy-in for improving analytics maturity is another key challenge. In many organizations that are struggling to become more mature, leadership isn’t yet aware of the full benefits of data analytics maturity and, as a result, hasn’t made it a priority.
Efficient, flexible data sharing is a vital quality of a mature organization, but concerns over security and data privacy can hinder efforts to break down silos and enable more modern, collaborative use of data. Organizations must, therefore, proactively strengthen data protection and security in correlation with changes to data-sharing policies and advancements in data analytics maturity.
While this is not an exhaustive list of all the challenges involved, it serves as an important reminder that businesses are often held back in their data analytics maturity by a range of issues.
Before implementing a modernized, mature data analytics program, organizations should first determine the level of maturity they aim to reach. This involves assessing the organization’s unique goals, challenges, business processes, requirements, users, and various other factors. This also involves defining the users for whom these capabilities are being developed, the scenarios in which they will be used, and users’ preferred means of accessing, analyzing, and reporting with data. These details can help establish the mature data structure that will work best for your organization.
Sometimes, organizations adopt emerging technologies because they feel pressure to conform to popular trends, even if that technology exceeds their requirements. Investing in advanced tools beyond your own requirements while in the early stages of maturity may cause more problems than it solves. Each organization must understand exactly what it is trying to achieve with more sophisticated data analytics and for whom before making an investment. Once these goals are clear, the best approach is to take small steps toward maturity first, for incremental gains. This is essential to minimize risk. It will also help to avoid creating additional challenges, which can occur when trying to disrupt too much at once.
Once a clear understanding of specific goals, requirements, and desired outcomes has been established, a strategy to modernize data practices and mature data analytics capabilities within the organization can be launched. There are some important steps that organizations must take to begin improving their data analytics maturity.
After defining the problems to solve with data analysis, the next step is ensuring that the correct people have access to the right data, allowing them to use data analytics to answer essential business questions and solve problems. Organizations should be clear and specific about goals and desired outcomes.
It is critical to confirm that the different types of data used in data modernization projects are accurate, up-to-date, and consistent. Data comes from internal and external sources in a variety of formats. As traditional data sources are increasingly supported by collections of data from newer sources, practices should be developed to assess and improve the quality of data throughout the organization on an ongoing basis.
After addressing issues of data availability and access to data, an organization can explore what sort of data analytics capabilities are required. Options can range from operational analysis and reporting to predictive analytics, all the way to self-learning, AI-powered entities. This decision will play a role in choosing which tools should be adopted and which processes need to be updated.
If there is a large deficit between current capabilities and the desired level of maturity, it may be necessary to perform a thorough gap analysis of all data sources, collection processes, storage and availability, and data management systems. This will illustrate what must be done to advance from the current state to the data maturity level you’re targeting.
Achieving these improvements in maturity will require moving away from basic manual processes for sharing data, like email and spreadsheets. Organizations leveraging intelligent analytics for enhanced data-driven decision-making will have begun to modernize data processes and adopt automated tools and systems to facilitate the optimal way for users to interact with data.
It is important to maintain an incremental approach to these steps. By undertaking a modernization initiative in this way, your organization will be able to test small changes and learn from mistakes quickly while moving forward on the path to greater maturity.
The value of using analytics to improve data-driven decision-making in the current business landscape cannot be denied. Moreover, by following the steps discussed in the previous section, additional benefits will begin to emerge. Not only will organizations gain greater data analytics maturity capabilities, but they will also:
The complexity of modernizing data practices and processes and successfully adopting and integrating new tools may seem daunting. Becoming more mature in data analytics capabilities is a crucial step in enabling a business to leverage data to its full potential and enhance data-driven decision making. As organizations advance in maturity, they will gain competitive advantages and empower their team to use data to fuel continuous improvement.
Bob Dunmyer, Partner
Guidehouse is a global consultancy providing advisory, digital, and managed services to the commercial and public sectors. Purpose-built to serve the national security, financial services, healthcare, energy, and infrastructure industries, the firm collaborates with leaders to outwit complexity and achieve transformational changes that meaningfully shape the future.