Digital transformation: 5 trends that could shift your strategy

If your digital transformation strategy doesn't reflect the most recent industry changes and related opportunities, reconsider your roadmap. These five trends could lead you to pivot
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As we slowly return to a post-COVID world, IT leaders should raise their periscopes and review industry trends. While many organizations were busy shifting to hybrid work, updating business processes, and adjusting to changes in customer needs, the opportunities to innovate, improve customer experiences, launch digitally enabled products, and mature machine learning capabilities may have taken a back seat.

Digital transformation is not just about taking existing business processes and making them more efficient. New technology capabilities and evolving customer opportunities require business leaders to rethink business models, products, target markets, and customer experiences. Your digital transformation may flounder and require you to pivot if it doesn’t focus on growth and customer opportunities.

Digital  transformation trends to consider now

Even healthy transformation programs and leaders should understand what’s changed and what’s trending. Where are competitors executing smarter and faster than your organization? Which technology capabilities are becoming more mainstream and worth researching or conducting a proof-of-concept? What process changes should your organization accelerate to prepare for new and changing opportunities?

Here are five trends that might drive you to adjust your digital transformation strategies.

1. Developing and maturing customer data platforms

For organizations recognizing that customer needs are changing and aiming to be more data-driven, customer data platforms (CDPs) can be a strategic investment to align organizational front-office practices. Organizations implementing these platforms aim to improve customer service, update product development strategies, modify sales priorities, and adjust marketing campaigns.

CDPs are essentially data warehouses and analytical tools that centralize customer information from CRMs, ERPs, marketing automation tools, and fulfillment systems. They provide a centralized view of the customer profile, interactions, and events and act as two-way information sharing repositories with other customer-interacting systems. There are industry-specific CDP solutions for retail and other B2C industries. Another option is to implement the CDP with a master data management platform.

Implementing CDPs is complex because of the number of stakeholders, business processes, and systems that capture and process customer information. But a recent report from the CDP Institute shows these programs are on the rise, with 52 percent of respondents feeding customer data into a central system, up from 37 percent in 2017. The bottom line: CDP initiatives remain challenging, but platforms are maturing and making them feasible to more companies.

[ Get exercises and approaches that make disparate teams stronger. Read the digital transformation ebook: Transformation Takes Practice. ]

2. Demonstrating the business impact of machine learning investments

There are many reasons organizations struggle to start AI and machine learning programs or fail to transition proof-of-concepts (PoCs) to production. But a recent Capgemini report shows that in 2020, 53 percent of respondents moved beyond machine learning pilots and PoCs in multiple use cases, up from 36 percent in 2017. The progress isn’t happening only in data-centric industries like technology and financial services; respondents from over ten industries, including often-lagging industries telecom, utilities, and energy, have deployed at least a few limited-scale AI to production.

Respondents in this report also report business impacts from their AI investments. Of the respondents identified as AI-at-scale leaders, 97 percent report quantifiable benefits, and 39 percent state benefits are higher than anticipated.

While many organizations report talent gaps in machine learning, maturing MLOps and ModelOps platforms help more organizations scale their machine learning processes, infrastructure, and operations. These platforms provide DevOps and SDLC capabilities to the machine learning lifecycle and aim to help more organizations build, test, deploy, monitor, and realize business value from machine learning investments.

With a growing number of machine learning lifecycle platforms on the market, I expect machine learning to be accessible to lagging organizations that have been slow to invest because of the required data and technology skills.

[ Want best practices for AI workloads? Get the eBook: Top considerations for building a production-ready AI/ML environment. ]

3. Modernizing applications with hyperautomation, low-code, and agile methodologies

Application development, modernization, and integration are central practices in digital transformations that help organizations launch new business capabilities, improve customer experiences, and drive business process efficiencies. Until recently, CIOs and IT leaders considered implementations as a build-vs.-buy decision or used an RPA platform to automate workflows. Many invested in maturing agile and DevOps to continuously deliver cloud-native microservices and applications when building applications. Then COVID hit, and more IT leaders pursued low-code and no-code platforms to accelerate application development.

Having multiple approaches to develop and support application development and integration is beneficial, but today, a growing number of options provide a complete hyperautomation platform. Hyperautomation app dev platforms have a mix of low-code, no-code, automation, and machine learning capabilities, provide out-of-the-box DevOps capabilities, and align the dev lifecycle to agile processes. Collectively, they can accelerate the development process and improve the productivity and quality of development efforts.

There are more options today to support application development and integration for organizations that require technology core competencies.

Does that mean more organizations can develop, support, and enhance applications without the complications of maturing software development processes? Can CIOs accelerate application modernization and build applications with less technical debt? These will be questions over the next few years, but there are more options today to support application development and integration for organizations that require technology core competencies.

[ Want more insights on emerging technology and digital transformation? Read more from Isaac Sacolick. ]

4. Driving IT efficiencies while supporting multi-cloud strategies

Many CIOs have a growing financial problem: They are adding new technologies to support data operations, machine learning, and cloud-native applications faster than they can shut down legacy systems, data centers, and business processes. Large enterprise CIOs will be in hybrid clouds for many years, and many view multi-cloud architectures and operations as their desired strategy to give their businesses the greatest operational flexibility.

The only way CIOs will be able to avoid a financial gap or an inability to support a growing technology portfolio is by finding efficiencies in IT operations. Put simply, IT operations will need to support more heterogeneous computing stacks with high service-level objectives and without disproportionately increasing costs.

How can this be done?

  • Automating more IT tasks and orchestrating processes for most standard operating procedures
  • Using AIOps tools to improve incident management KPIs
  • Investing in DevOps, CI/CD, IaC, automated testing, and shifting-left security practices
  • Integrating ITSM, DevOps, SRE, and agile tools and processes to improve collaboration
  • Selecting “single pane of glass” tools that function across public and private clouds

IT organizations investing heavily in digital transformation without balancing their efforts by driving internal efficiencies are likely to create potentially insurmountable technical debt mountains.

[ Read also: 6 misconceptions about AIOps, explained. ]

5. Replacing waterfall PMOs with agile value stream management

Every year I advise CIOs to stop overpromising, reduce the number of number-one priorities, and extend agile methodologies into the program management office (PMO). Developing roadmaps and demonstrating business value is critical for CIOs leading multiyear digital transformation journeys, but asking teams to commit to top-down strategic plans and plan quarterly is antithetical to driving agile cultures, processes, and mindsets.

CIOs can’t be caught in a divide between agile practices focused on incremental change and how business leaders manage strategic priorities and business roadmaps. Driving the organization’s culture forward will require CIOs to challenge their PMOs to work directly with agile teams, adopt continuous planning practices, and review emerging value stream management tools.

Digital transformation failures and reboots are increasing. IT leaders should explore and leverage the trends behind them to drive their programs’ success.

[ Culture change is the hardest part of digital transformation. Get the digital transformation eBook: Teaching an elephant to dance. ]

Isaac Sacolick, President of StarCIO, guides companies through smarter, faster, innovative, and safer digital transformation programs that deliver business results.

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