Data and analytics tool migration can help growing businesses deal with evolving requirements for data-driven insights. Modernizing your data and analytics platform can also reduce the security risks and costs, associated with using multiple analytics tools. However, migration is a complex process. Whether you are looking to consolidate redundant tools or upgrade from legacy systems, it is necessary to be aware of potential pitfalls that lead to failures.

In this article, we have discussed some of the common pitfalls that can lead to budget overruns, extended deadlines, user confusion and change resistance. We have also highlighted, how we help you to avoid these pitfalls and ensure success in your journey.

  1. Wrong Migration Choices

    Wrong choice of tool, infrastructure, capacity or migration approach can derail your project. A properly designed tool comparison exercise helps in identifying the best-fit tool for your organization in the context of your requirements. Also, ensure that you choose the right infrastructure and sizing for your analytics tool to avoid performance bottlenecks and SLA breaches later on.

    Infocepts brings depth and breadth of experience across all modern analytics platforms to help you make the right choice of tools, infrastructure, capacity and approach to minimize the design gaps and risks early in the process.

  2. Failure to Align Key Stakeholders

    Successful analytics tool migration requires stakeholders to understand what success looks like and agree on structured approaches to execute migration initiatives. Before starting, it is vital to create a comprehensive roadmap and an optimized implementation plan to align all key stakeholders. Clear prioritization and readiness assessment is crucial to avoid any surprises.

    Infocepts brings in experienced teams to help you align your stakeholders and manage the change effectively.

  3. Lack of Necessary Expertise

    A BI tool migration project does not just require technical skills in the source and target BI platforms. Most platform migration projects may involve moving to the cloud, rationalizing your BI inventory and taking advantage of new capabilities to reap the full benefits of the new tool. To do this – in addition to tool skills, you need to factor in skills such as advisory, cloud, data modelling, data storytelling, change management, automation, optimization and more.

    Infocepts brings end-to-end data analytics capabilities and specialized migration teams to help you succeed in your migration journey.

  4. Taking Adoption for Granted

    Your users have likely been using your current analytics tool for many years. They are well aware of its shortcomings and have adapted to it, forming bad habits from having to make do with the current system for such a long time. These habits can be hard to break, and some users may even initially reject the new platform.

    This is why training is critical to the success of an analytics tool migration process. It is not enough to teach your users how to use the new platform step by step. Rather, training should be viewed as an ongoing effort to make your key users proficient in the new tools. This then creates cost savings and long-term process efficiencies.

    Infocepts offers training combined with user adoption support to ensure post-migration success.

  5. Losing Control of Schedule and Budget

    It is expensive to manage multiple platforms or maintain a legacy system. The costs of support and upgrades, license fees, hardware, administration, training, and maintenance can quickly spiral as time goes by. But while analytics tool migration and modernization offer potential for savings, it is also easy for companies to lose financial control of a migration project because of inefficiencies during execution.

    Infocepts offers accelerators that can be combined with pre-built migration toolkits to migrate analytics ecosystems efficiently. We can help your company get the most out of your investments in new platforms while minimizing costs associated with change.

Talk to us about our holistic platform migration approaches that accelerate the process and reduce the risk of failure.

Get Started with Infocepts today!

Recent Blogs

With the increase in data and a rapidly changing technology landscape, business leaders today face challenges controlling costs, fulfilling skill gaps for employees, supporting systems and users, evaluating future strategies, and focusing on modernization projects.

Here we discuss six reasons why organizations are embracing managed analytic solutions that rely on experts to build, operate, and manage their data and analytics services. These are based on the recurring themes which we have observed and experienced while working with our customers.

  1. Keep costs low: Total cost of ownership for running and maintaining D&A systems has several cost elements like staff costs, operational costs, software + infrastructure costs, and (intangible) opportunity costs like technical debt and avoidable heavy lifting. While cutting costs in the short term may lead to some immediate gains, cost effectiveness in the long term and on a sustainable basis is the end goal. The right way to approach and achieve guaranteed, predictable cost savings is through a potent combination of automation, talent, and process improvements.
  2. Improve system stability and reliability: Missing SLAs, performance issues, frequent and persistent downtimes, and an inability to comply with regulatory requirements are the usual suspects when it comes to areas giving sleepless nights to leaders navigating enterprise data and analytics (D&A) systems. Improving system stability and reliability requires long term planning and investments in areas like modernization of D&A systems, data quality initiatives under a larger data governance program, RCA with feedback, 360-degree monitoring and pro-active alerting.
  3. Intelligent D&A operations: You may want to drive operational efficiency by reducing the piling automation debt, bringing in data-driven intelligence (and human ingenuity) to achieve AI-driven autonomous and real-time decision making, better customer experience and as a result superior business outcomes. An example would be on demand elasticity (auto scaling) to scale-up the processing power of your D&A systems, based on forecasted demand due to seasonality in the business based on past trends.
  4. Focus on core business objectives: You may need to focus on your core business objectives and not get stuck in the daily hassles of incident management and fire-fighting production issues. We have seen that reducing avoidable intervention from your side becomes difficult, especially when you are managing it in-house or using a managed services vendor operating with rigid SLAs. A recommended approach would be to engage with a trusted advisor to figure out the right operating model for managed services with shared accountability and define service level outcomes. This will enable you to devote attention to more innovation focused and value-added activities which drive business results.
  5. Get the expertise you need: Given multiple moving parts involved in successfully running D&A systems, and the sheer flux of technological changes, your business needs the ability to tap into a talent pool easily, and on-demand. If executed well, this does wonders to your capabilities in managing D&A systems and achieving desired business outcomes.
  6. Improve user experience: This is the most important and yet often the most neglected aspect in a lot of cases. In the context of managed services, an elevated user experience entails data literacy, ability to leverage tools to the fullest, clarity on SLAs and processes, trust in data quality, ability to derive value from analytic systems and hence adoption.

Infocepts Managed Services solution helps organizations achieve one or more of these motivations. We help drive digital transformation, handle legacy systems, reduce costs, enhance innovation through operational excellence, and support scaling of business platforms and applications to meet growing business needs. You can rely on our D&A experience and expertise to build, operate, and run analytic systems which help to drive outcomes quickly, flexibly, and with reduced risk.

Get in touch to know more!

Recent Blogs

As companies mature and find themselves with growing needs for more capable or more pervasive business intelligence throughout their user base, sooner or later they will have a need to migrate their analytical applications from one business intelligence (BI) platforms to another.

What motivations does a business have to migrate their BI tools?

The reasons that motivate these migrations are quite varied and range from moving away from manual analytical processes that take a long time to produce, to consolidating a large array of tools that drain company resources in licensing, maintenance and operational costs. Other companies may try to take advantage of more modern capabilities, such as visual data exploration or even dip their toe into Artificial Intelligence (AI) or Machine learning (ML) to Natural Language Querying. You might even have the systems generate analytical application automatically, using Natural Language Generation.

Whatever the motivation, there will most likely be hard work ahead, business processes that need to be protected from the disruption of a migration, and there will be expectations to be met, both from existing stakeholders and potentially new stakeholders, who will be motivated by the promise of what the new BI platform and the applications that can be built on it can bring.

What team is responsible for BI migrations?

While the need to migrate from one BI tool to another will most likely be driven by business goals, and business users, the technical teams responsible for the migration will not always have the complete picture of what these desired goals and outcomes are that drove the migration in the first place.

It is for this reason that we recommend beginning any migration effort with gathering consensus, and aligning all stakeholders on what the migrated application should be like. How is it going to meet the expectations of advanced capabilities, improved user experience or reduced total cost and reduced time to value for the applications that will be built on the new platform.

It is also important to understand what are the positive processes in place that should be maintained from the current way of doing things. If the migration is from a manual approach of generating reports and analytics, the migration team may be in luck as there is not much of a precedent in terms of functionality and performance and the BI platform will be perceived as leaps and bounds of progress compared to the previous way of doing things. However, if as in most cases, there is an incumbent BI Platform, there may be more risk to the migration.

What common problems can occur in a business intelligence migration project?

There will be users of any system who are comfortable with the way they use and consume their analytics, and at times, many iterations to optimize performance and the data model have taken place that may not be suited to the new platform. Worse, sometimes the new platform does not support scalability features, such as application partitioning or more sophisticated data volume management. This can happen when the team that identified the need for a migration prioritized front end visualization or ease of use and leave some of the performance and scalability issues to be resolved by the technical teams implementing the migration.

Another common source of challenges is when a technical team is given extremely tight deadlines, and this pushes re-utilization of existing data models and backends that may not suit the new platform optimally. Whenever this happens, an application can be built, but it may not be received as well because it will have increased the complexity of the systems or be forced to work with a data model that is not optimized for it and performance issues may be seen.

There have been books written on the subject of “What You Know Can Hurt You”, in this case, this is analogous to “The data model that you have can break your BI System”. It is therefore important to do as much as possible to build the migrated application in a way that allows for backend analysis and updates to ensure that the front-end application will run as efficiently as possible. It is easy to see that for greenfield applications, this problem would not be there.

What strategies are worth investing in for a successful BI migration?

Nonetheless, it is possible to manage through this landscape with a properly managed and proven migration methodology. One of the most important milestones of the migration project is to generate a clear picture of the desired end state and share it with key stakeholders to the project to set expectations correctly both for the end system and for the required support and resources that will be needed, identify barriers to overcome and align project goals.

If working with the right partner, there may be an opportunity to use different assets and accelerators that can both save time and increase the quality of the migrated platform. The ability to generate automated metadata reports and analyze the existing applications can be a great time saver, as well as the ability to automate testing and data quality for more efficiently moving through the acceptance phases can also be of great value.

As it should be clear by now, there can be perils in the path to BI platform migration, but with the correct risk mitigation strategies and correct partnerships, your company can do a great deal to maximize their chances of success and realizing the promise of improved functionality and access to the Analytical Applications that enable the daily business processes.

What have you learned from past BI Migrations?

Let’s take a look at a past migration we did for an American mass media company. We partnered with them to migrate and optimize their analytics tool portfolio to enable self-service, simplify operations, and reduce overall total cost of ownership (TCO). They wanted to consolidate their BI applications to achieve improved insights, maintenance reduction, and a superior user experience. However, they faced a common challenge of what tool to choose. There has been a recent explosion of available BI tools from MicroStrategy to Tableau to Domo to Business Objects to Power BI and others.

To solve this first challenge our team researched and evaluated many of the BI platforms in use to understand its supported business processes. We then provided a thorough cost analysis to the primary platform owners. It detailed licensing, maintenance, and infrastructure to reveal its true TCO. In evaluating platforms and applications that would be migrated— coupled with the expected savings and estimated project duration—we used this information to garner support for a platform consolidation effort.

Over the next 12 months we helped our client redesign, optimize, and migrate over 1000 reports and dashboards across five BI platforms. These supported sales, operations, and CXO reporting, as well as delivered a platform that provides self-service capabilities. Through this BI migration our customer saved over $1M, was able to reallocate personnel and infrastructure, reduced costs, and accelerated its overall time to value.

How can a data and analytics partner like Infocepts help in our BI Migration?

Many enterprises are challenged with high technical debt of legacy BI platforms and overlapping analytic capabilities. Enterprises want to modernize their platforms to reduce cost, be more agile, and to provide better user experiences. But migration of BI platforms is often a complex process, involves change, can pose a business risk and disruption.  Hence, migration to a new platform requires meticulous planning, thoughtful rationalization, intuitive design and execution excellence.

Infocepts is a one stop shop solution for customers who want to migrate their databases, ingestion, and/or BI and analytics tools using a proven repeatable approach. We have the expertise to make assessments, trace data, recommend cutover paths, and manage change results in the best migration strategy. In our rich experiences of large-scale projects for enterprise customers. We have applied best practices, solution accelerators, and cross-functional experts to automate the process and deliver predictable results with operational flexibility—all while avoiding unnecessary costs and hassles.

Want to learn more about the Infocepts D&A Platform Migration?

Infocepts is a data solutions firm which uses services to drive business outcomes with data and analytics. Through a solution-oriented approach, we guide the modernization efforts of our customers, enabling them to make truly ‘data-driven’ decisions so your users can make smarter decisions and businesses can achieve better outcomes.

To find out how Infocepts can help you during your business intelligence migration and beyond, connect with one of our experts directly.

Recent Blogs

Are you’re considering changing your BI platform? Or looking to add a new one to your tech landscape? Perhaps, you have made the decision on what BI tool to implement but need a better understanding of the process in and of itself. Well, you’re in luck. This blog provides an overview of the potential pitfalls when migrating away from or to a new BI tool and opportunities that exist for those who do.

Why do I need to migrate from one BI tool to another?

First, let’s understand why a business, like yours, might need to change your BI platform. Usually, it stems from changes in business demands or technology challenges. Your business might need to move to a newer legacy platform or application, or migrate from one BI tool to another, or simply just upgrade your current BI tool. Or, if different departments have brought one specific BI tools for their needs, your IT department might want to consolidate and organize the tech stack to a single platform.

More than likely, the incumbent BI tool has been around for some time in your department, and even with its shortcoming’s your users are comfortable with its quirks and will be sad to see it go.

What common challenges exist in a business intelligence tool migration?

Change is usually a good thing; but change can also represent a number of challenges across various levels. Trying to do everything at once, can cause you and your team to feel overwhelmed and not want to move forward. Preparation combined with experience is a key factor when planning for a successful BI tool migration. At the end of the day a BI migration can be a big project, consider the following potential challenges that may arise:

  1. Workflow disruptions – Expect disruptions in daily workflow and its impact on deadlines, near and long term.
  2. Habits both good and bad – As legacy BI tools are retired, bad habits formed by users can remain in place.
  3. Additional workloads for project participants – Migrating and implementing a new BI tool requires users to participate in the new process. Adding additional tasks for users can create roadblocks. Create a plan to ease the burden on the users.
  4. Front load the process to avoid changes downstream which translates into rework, increased costs and frustrated stakeholders – Plan, plan, plan and did I mention planning? Consider the downstream impact of conducting proper due diligence combined with collaboratively planning the migration.
  5. Training achieves acceptance and proficiencies – proper training means less rework which translates into happier business users.

What opportunities exist with a BI tool migration?

Organizations, and individual users, can benefit from an organizational change of BI tools by recognizing an opportunity for growth. It’s not easy to get the whole organization on board with the migration but educate your team about the positive benefits that widespread changes will have on the organization. By uniting your data in one tool, and provide other chances for growth, such as:

  1. Avoid bad habits by replacing with new processes – We all have bad habits. However, taking advantage of a new tool is a golden opportunity to change old habits into good, successful processes. Seize this opportunity to create new, better habits
  2. Workloads and workflows impacts – Different tools require different paths to completion and can cause angst with users. Communicate the organization’s objectives and reassure users of the end goal how this will help the organization. Adding additional responsibilities, such as participating in a tool migration likely will increase stress within an organization. Careful resource planning is strongly encouraged to help maintain a work balance.
  3. Training is critical to successful migrations – As the tool use and users mature over time, training programs need to evolve as well. Training is not a stepped process but rather an ongoing, long term process. Solid training programs will yield tool proficiencies which in turn creates process efficiencies and translates to cost savings.

Are you ready to migrate?

Contact us, and our experts will answer your questions about technologies, migrations, and how to get started so that your organization can accelerate, automate, and advance data-driven decisions.

Recent Blogs
Back to Blogs

This article was first published on Medium, read the article here.


On February 2, 2021, the Groundhog Day, Punxsutawney Phil emerged from his snowy burrow to predict six more weeks of winter! “We have all passed through the darkness of night but now see hope in morning’s bright light. But now when I turn to see, there’s a perfect shadow cast of me. Six more weeks of winter there will be.” narrated Phil’s top-hatted handler.

If you are an executive responsible for Data & Analytics Modernization in your firm, you may well be living what Phil Connor, played by Bill Murray, lived through in the 1993 classic “Groundhog Day”–a time loop. Except, you find yourself in what I call “perpetual modernization cycles”– a result of exponential growth of different forms of technological progress over the past two decades–something suggested 20 years ago by American futurist, Ray Kurzweil, in his renowned essay, “The Law of Accelerating Returns.”

The Modern Groundhog Day – Start of Perpetual Modernization Cycles

As we embark on a new decade, I can’t help but think about what IT executives will have to deal with in the coming years. We already face relentless pressures to modernize. Business is creating more demand for speed, agility, and capabilities; while, IT is facing supply pressures due to changing technology, ever-growing data sources, and security threats. According to Gartner, by 2025, 90% of current applications will still be in use, and most will continue to receive insufficient modernization investment.

To address modernization, companies are using approaches that look something like Rehash, Rehost, Rebuild, and Replace using an effort and value trade-off. There is no one-size-fits-all approach and you are likely trying multiple options, like what Phil tried, using a combination of technology, functionality, and architecture adaptations to deal with your situation. But even before you finish your current project, you are already facing pressures either from the demand- or supply-side. Welcome to the Modern Groundhog Day!

During my 20-year experience supporting enterprise customers in the public and private sectors, I have lived through multiple cycles myself. For example, at the Department of Health and Human Services, I supported multiple versions of web-based systems used to collect, report, and analyze–clinical, performance, and administrative data from over 13,000 health center service delivery sites providing primary care to over 25M patients nationwide. At Infocepts, we are modernizing data-driven capabilities for multiple retail, media, data syndication, financial, and health companies across the globe. Whether it is preparing customers for the Olympics, or liberating them from legacy monolithic BI platforms, or using AI-driven insights to connect retail businesses with consumers–we are constantly driving modernization initiatives.

Like Phil Connor, we evaluate each experience and apply our learnings to future endeavors improving outcomes for our customers. Here I share three learnings and ideas to deal with the Modern Groundhog Day.

Three Implications of the Perpetual Modernization Cycles

The scope of modernization programs is becoming broader. Companies are no longer looking at “lift and shift” approaches to upgrade technologies, instead, becoming more mindful of revisiting end-to-end business needs to deliver the intended benefits such as improving customer experiences, reducing TCO, or improving operational effectiveness. But that means more decision points along the way, often beyond the IT organization, and thereby more time to execute. Agile techniques and modern technologies such as cloud and low-code platforms help with reducing the cycles times and time-to-value, but large-scale legacy modernization programs often span across multiple calendar years, despite what vendors want you to believe.

  • 1. Longer cycles mean business keeps waiting for intended benefits

    Proponents of agile techniques–and, I am one of them–often advise clients to focus on some variation of “minimum operating capability.” After the initial discussion on the scoping and prioritization, the program management leadership focus on the question of, “What is the minimum capability necessary in production to see value?”, and allocate resources to get to that. But that isn’t the finish line and success shouldn’t be measured based on that metric alone.

    An Infocepts data syndication customer operates large-scale data factories; they are in the process of modernizing them–a few processes at a time. But the consumers of these factories residing in distributed business units are facing massive operational pressures to cut their annual operating budgets. The challenge is that the processes that haven’t been modernized continue to drain resources and perhaps the program management leadership is oblivious to that.

    So, the key takeaway here is to measure executive success of modernization programs based on the initial time-to-value, incremental cycle times, and total time-to-value with corresponding TCO numbers.

  • 2. Organizations are accumulating redundant capabilities faster than they can retire their legacy ones

    To take advantage of technology evolution, companies are adopting new systems for engaging their customers and/or new systems for innovation while maintaining their systems of record. In a rapidly shifting market, vendors are offering niche capabilities to create differentiation to acquire customers. This is particularly true in the analytics landscape where platforms such as Tableau penetrated from the front-end visualization layer, closer to business users, and then organically matured toward established enterprise platforms such as MicroStrategy and Business Objects. The same is true now with products such as ThoughtSpot that are focused on search-based analytics, continuing to bridge the gap between the end-user and insights. This trend coupled with inorganic changes to customer data & analytics landscape due to internal restructuring or M&A results in multiple redundant capabilities.

    An Infocepts media customer ended up with six enterprise capabilities to include MicroStrategy, Tableau, Qlik, Power BI, Business Objects, and Looker! Clearly, they needed to eliminate redundancy and save costs. Solving such a problem requires a few months for the rationalization, then migration, and ultimately timely decommissioning of redundant capabilities. It took our team 12 months to cut down the number of systems from 6 to 3 and save them $750K in annual costs.

    The key takeaway from this is to maintain up-to-date information on current tools to support future rationalization efforts and tie decommissioning to project success. Maintain basic information such as the core purpose of the tool, usage data, financial data, and intangibles such as customer expectations to speed up rationalization efforts and to inform critical cutover milestones based on software renewal dates.

  • 3. Leaders (and teams) are grappling with multiple responsibilities

    Regardless of how you are organized, IT executives typically deal with responsibilities to include strategy, experimentation, development, operations, and adoption. For any modernization project, you must address these elements–how you choose to do may vary. Factors that play into how leadership and teams are organized include the need for legacy knowledge, aspirations and interests of leaders and team members, and long-term vendor agreements and relationships. But you can see how quickly the number of responsibilities multiply with the result that teams start to locally optimize for their available capacity and the overall velocity suffers.

    For example, data migration requires knowledge of business rules prevalent in the legacy system; teams want to work on newer technologies and not be boxed into maintaining legacy tools; a leader may be interested in demonstrating results for their growth; and, companies may be locked up into master service agreements with global system integrators to gain preferred rates with penalty clauses for deviating! These dynamics hamper modernization success and the longer the cycles last, the more complex they become. The result is that customers pay more for their modernization efforts than initially forecasted.

    The key takeaway here is to ensure that companies take an end-to-end approach for modernization outcomes and establish cross-functional leadership teams to devise and execute the strategy to minimize cognitive biases from influencing enterprise roadmaps. Modernization is more than a technology or capability upgrade.

Three Ideas to Deal with Perpetual Modernization

As companies deal with multiple modernization cycles, I anticipate them codifying their holistic approach in an Enterprise Modernization Life Cycle (EMLC) much like the Software Development Life Cycle (SDLC). The key is to connect the EMLC to their enterprise roadmap that advances them along their pursued business strategy. If you do these two things, you’d be able to find comfort in its predictability and take advantage much like how Phil made use of each of his new days once he found his purpose!

  • 1. Don’t reinvent the wheel

    With the proliferation of cloud-based “services”, it is becoming harder to quickly put them together to construct enterprise capabilities. This is in part due to the paradox of choices and our desire to make the right choice without negative repercussions. Some vendors are making it easier for businesses by offering “Anything-as-a-Service.” But if you are responsible to advance your enterprise roadmap you can look for building blocks in the form of “turnkey projects” or “reusable capabilities” and compose your bespoke solutions rapidly. In this case you are counting on the experiences and learnings from other parts of your organization or from the market to reduce your time, costs, and risks with success.

    Consider a situation where you want to find an alternative to your legacy analytics enterprise platform and move to a modern one. This is likely not a very “common” project for your team. In such situations, you may look at experts that do turnkey migration with precision enabling you to focus on making critical choices, supporting current operations, and preparing for the cutover. As an example, Infocepts offers a BI migration solution that consists of a cross-functional team with a proprietary methodology and tool refined over successful execution of 100s of such projects to guarantee your success. Similarly, we have a solution to move workloads from legacy data warehouses such as Netezza to Snowflake within a few weeks.

    You may find such solutions within your own organization as well. For example, at one of our customers, teams from different business units are sharing foundational capabilities for a CI/CD DataOps pipeline so they can augment it, rather than starting from scratch.

  • 2. Get your teams out of the DIY mindset

    You should encourage your team to think outside the box when making progress toward modernization initiatives. This is to overcome situations where the same team is signing up for several capabilities and then prioritizing them locally. Teams with highly capable technical talent often think they can learn new technology quickly and then implement at scale. Or like mentioned earlier, some companies may be constrained by their master service agreements with penalty clauses. Regardless of your situation, you should evaluate your team’s current capacity and focus, and what you need to accomplish toward your modernization, and determine how to allocate responsibilities creatively.

    For example, one of our customers wanted to free up the capacity in their operational team toward modernization. They engaged Infocepts to do a turnkey RPA automation using UiPath to reduce both the time and effort to execute quality assurance lifecycles for their report factories. In just 3 months, we helped reduce the QA effort from 9 FTEs to 3 FTEs while increasing their coverage to 100%.

    At another customer, our team helps in experimentation while they keep the enterprise implementations closer to their core team. This could be a way forward to try out multiple options for newer capabilities such as Data Science adoption. The reason is that a single team or organization likely does not have the time or resources to exploit the breadth of scenarios during evaluation to help them make informed choices.

  • 3. Consider alternative solutions

    The last idea prompts leaders to challenge their teams to explore alternative solutions to costly modernization endeavors. Cohesive agile teams sustained for long durations may get very productive, but there is a risk of them succumbing to groupthink. Let me illustrate with two examples.

    Consider a need within your organization to provide an analytics hub to make analytics assets from different capabilities more accessible to your users. If your team is used to building custom solutions or is currently supporting a custom portal, their default thinking would be to add features to their portal or build a new one. In such situations, you must challenge them using the build vs. buy rubric. For example, solutions such as Metric Insights and ZenOptics can provide hubs in a few weeks rather than going through a costly and risky development endeavor.

    Or consider a situation where you are looking to optimize long-running ETL jobs designed several years ago. Again, if your team is used to traditional warehousing approaches, they may Rebuild the ETL packages with automation. But such an approach may miss out on the Replace option using emerging technologies such as Incorta that enable direct connects to data sources with real-time aggregations and in memory transformations.

When Will Modernization End?

Kurzweil may well be on to something. We know for a fact now that the rate of technology is certainly changing rapidly. But for that rate of change to translate into accelerated returns for our businesses, we all need to think differently going forward. Rather than looking to modernize quickly, we need to find ways to remain modern. This can be done by adopting evolutionary design thinking, growth mindset, agile leadership, and reliance on proven reusable assets to charter our own course. Maybe, like Phil, we will wake up one fine day and find that we’ve transcended the modernization loops.

Recent Blogs