Digital transformation is one of the strategies enterprises are embracing to stay competitive in the industry 4.0 era. Organizations are investing heavily in technologies like the cloud, AI, or IoT for purposes such as streamlining business functions and processes, increasing customer value, creating new product lines, or shifting to a more competitive business model. Sadly, only about 30% of digital transformation initiatives are achieving their objectives.
With the right digital transformation approach, however, you can get it right the first time. Here are 10 most costly mistakes to avoid while at it:
1. Not Understanding Advanced Analytics and Staff Requirements
It's a mistake to dive into the world of advanced analytics without a proper understanding of what it entails and the value it brings. To get it right, companies need to figure out a lot more than just traditional business intelligence tools and methods. They need to study and understand the sophisticated tools and methodologies necessary to support the autonomous or semi-autonomous analysis of massive amounts of data. For example, organizations may use techniques such as big data mining, machine learning, visualization, pattern matching, sentiment analysis, or predictive analytics to extract in-depth insights and forecast trends, behavior, or actions.
Planners of digital transformation in an organization should also take into account staffing requirements. That includes identifying the skills required (in-house and externally) to implement and leverage advanced analytics. As such, organizations need to think about the training needs of end-users, leaders, and analysts needed to develop or utilize the proposed digital solutions or platforms.
2. Failure to Assess the Feasibility of the Digital Transformation
Many companies are making the mistake of developing new digital solutions, infrastructure, or processes before assessing their feasibility. So, they're unable to hardwire value into their digital projects. And they're paying dearly for that when those initiatives stall or collapse without justifying their costs, and in many cases, after eating into resources meant for other mission-critical business activities or functions.
Assessing the viability of any proposed digital transformation entails working out the interests of all stakeholders, including investors, stakeholders, and customers. It also includes figuring out time horizons for each initiative (i.e., determining how long specific projects will take to pay off). Likewise, planners must figure out when it's smart to pull the plug on digital investments that don't work quickly enough.
3. Lack of Strategy Before a Few Case Studies
Companies that don't base their digital transformation on the right business strategy are unlikely to achieve their objectives. Such organizations may fail to build multiple prototypes and to experiment extensively before picking the best product. But to succeed, they need to define their business goals first. Then, they can test and figure out the digital technologies most suited to their stated goals. It's also essential to measure and show how advanced analytics is helping to satisfy customer expectations or improve the customer experience.
4. Lack of "Translators" to Bridge Business and Analytics
Leveraging advanced analytics requires expertise that may not be available to a business before adopting AI-driven operations. As such, many organizations don't have "translators" on hand to help with the application of analytical methods to business problems. This situation leaves marketers, sales reps, and customer-facing staff without a meaningful way to extract deep insights from the massive amounts of data streaming from various digital sources.
5. Inability to Generate Buy-in Within the Organization
Resistance to change is a major obstacle to digital transformation within many organizations. Regrettably, many leaders are unable to generate buy-in and get staff at all levels to rally behind potentially-game changing ideas. For example, employees may oppose digitization fearing that it may take their jobs. Likewise, senior management may not readily recognize the value or benefits of abandoning human-centric workflows or legacy systems that seem to be working just fine.
6. Isolating Analytics From the Business and Lack of Big Data Integration
Separating analytics from the rest of the business doesn't make sense since the latter consumes the former. As such, it's counterproductive not to give relevant business units real-time or near real-time access to analytics capabilities.
To succeed in transforming operations through digitization, organizations should consider abandoning silos. They ought to be streaming data (from different sources) into a centralized place to provide a unified view to all teams.
7. Time-Consuming Data Cleaning Tasks
Most data scientists spend the bulk of their time preparing rather than analyzing data. That's usually the case when an organization has deployed technologies like the cloud and IoT without a proper data cleansing strategy. Since a large proportion of the big data coming from such sources is unstructured and unclean, businesses need advanced tools to correct and validate it quickly before analysis. Only then can enterprises extract maximum value from their data.
8. Failure to Measure the Impact of Analytics on the Bottom Line
Some organizations are not quantifying the impact of analytics on the bottom line. Thus, they're unable to tell or show the difference in revenue or profit that a specific digital product, process, or business model is making. But a viable digital transformation strategy must incorporate performance metrics and ways to prove value and ROI. By tracking the outcome of analytics, decision-makers can implement corrective measures in time when a plan is not working as it should.
9. Not Having an Enterprise-Wide Adoption Plan in Place
Most unsuccessful technology-driven change initiatives lack an enterprise-wide adoption plan. The missing ingredient causes some departments or teams within an organization not to leverage newly adopted data or digital solutions. To facilitate the optimal adoption of AI, for instance, there should be a way for employees to compare the results of AI-driven and human-centered decision-making. This way, leaders can get everyone (not just IT or marketing staff) to support their change initiatives.
10. Failure to Support Business Accountability in AI-Driven Change Initiatives
Not making business units responsible for the success of digitization is a recipe for failure. Responsibility here means having business leaders assign roles and take charge of the change initiative throughout the development lifecycle. For example, relevant business teams should be able to track and demonstrate AI performance metrics. Thus, accountability shouldn't be the sole responsibility of analytics staff.
If you’re a CEO, Business Owner, or Executive in need of digital transformation guidance or a high-performance business coach, contact us to see how we can help take you and your business to the next level.