In my previous blog “4 Legacy Principles Applied to a Digital World” I observed the wisdom of only implementing digital change where it will add the most value, facilitate a more intuitive user or customer experience, or reduce costs. It is important to recognise that there is a point at which the investment is greater than the return. Specifically, I stated –

“It perhaps seems a little counter-intuitive to limit digital transformation when so much is now possible. However, by recognising there will always be constraints like a budget to consider, and that change can still have unforeseen outcomes, then the changes most likely to provide the greater customer and employee satisfaction, cost benefit and ROI should be targeted.”

It is equally important when running any project to ensure that it is delivered within budget, on time and with the agreed scope. Otherwise, any ROI benefit will be diminished by the cost, and the business impact could adversely be affected by delays, poor quality or failure to meet expectations. Digital transformation projects are no different in this respect; arguably they are more visible and the impact of success or failure greater.

In this blog, I elaborate both on how digital innovation can be used to provide the means by which targeting digital transformation effectively can be achieved, and how it offers the opportunity to transform the way we deliver projects.

Of course, measures like CSAT/NPS[1], ESAT/EPS[2] and ROI/TCO[3] can help in understanding the effect of change or justifying the need for change. However, the former are after the fact and the latter are increasingly difficult to predict precisely due to the impact of digital innovation. So how can these be anticipated or forecast more accurately?

The obvious starting point is to take your own advice. It does not seem unreasonable that if you are advising a customer (either an internal business unit or an external client) on best practice, then you would follow this yourself.

For example, if CRM and CEM[4] are core building blocks of any customer engagement strategy, then the best practices that result should also lead to applying similar principles to your own organisation. So this would mean implementing a best in class CRM system with the philosophy, processes, and strategy that focus on the customer experience.

Similarly, if an organisation is run efficiently and effectively through business intelligence, then analytics plays an essential role in providing the data to achieve this. Therefore, by implementing analytics best practices and a leading business intelligence solution, an organisation is better placed to bring the right products and services to market, with a go-to-market strategy aligned with the correct customer segment.

Traditional market segmentation has been used to direct campaigns to the most appropriate customer demographic. With the advent of digital marketing, this has become increasingly dynamic, interactive and targeted through personalization.

Furthermore, big data analytics facilitates this by driving the marketing of products and services through identifying hidden patterns and market trends which enable greater insight into, for example, customer preferences or buying behaviour.

This may lead, for instance, to transforming customer service through implementing an Intelligent Virtual Agent (IVA) on an organisation’s customer web portal or mobile app to reduce the cost to serve, or transforming the customer experience through increasing the efficiency of the digital marketing effort. Thus creating a seamless customer journey from an enquiry or expression of interest through to a purchase, cross-sell or retention, and ideally a referral from a happy customer.

However, this is very operational in nature. The focus is entirely on transforming the core front-office business functions, particularly with respect to sales, service and marketing, or the back-office functions like HCM or SCM[5]. Are there ways in which we can digitally transform the projects implementing the digital transformation themselves, or even the manner in which such projects are identified and defined?

Furthermore, are there any lessons the IT industry can learn from how Digital innovation is being used to define and deliver projects in other industries, like construction?

Consider the complexity of large-scale construction projects, such as building a skyscraper or an airport. There are multiple stakeholders, working in different geographies or time zones, in order to deliver materials precisely meeting the designs within a specific time window. Delays to just one delivery can have a major impact on the overall schedule and a knock-on effect on other stakeholders.

It is vitally important to manage the logistics efficiently, from the overall project delivery to the minutia of coordinating the trucks delivering materials to the construction site in the right order and with the greatest efficiency. There is a dependency on information availability and distribution between managers and stakeholders.

Many such construction projects are now leading in the use of digital technologies to increase the efficiency and reduce the risks of delivery. Datafloq report how construction companies are using Big Data to help deliver their projects on time (Rijmenam, Mark van, 2015). It is enabling them to use multiple data sources and active site information to both plan and accurately track their budgets in real-time. Similarly, they are using Cloud solutions to share the latest documents globally with team members, contractors and partners in real-time, regardless of time-zone or geography. This minimises the risk of delays, increases productivity and improves cost-effectiveness.

In large and complex construction projects the efficient coordination of resources, like equipment, materials, and people, is of significant importance. The Datafloq report also states that the use of sensors in equipment, telematics, GPS devices and the real-time monitoring of this data has even enabled projects to be delivered ahead of schedule.

Anthony Bryda of Proven Solutions, LLC writes that the success of capital projects, like construction, can be improved by using Big Data and proactive analysis (Anthony (Tony) Bryda, 2015). Through the collection of data on thousands of projects, tools have been built by the Construction Industry Institute (CII) to consider ‘project readiness, proactive team dynamics, and data on safety, cost, schedule predictability, and quantity benchmarks. These tools and data in combination with other completed capital project industry data on cost and schedule competitiveness and engineering metrics can be used to compare projects in the planning phase with results of completed projects to provide a better understanding of projects in the planning phase.’

Proactive analysis of the project data at a particular stage of the project lifecycle, compared to the benchmarks provided by the CII tools, enable the project manager to make better planning decisions. He also writes that the status of the team dynamic can be assessed through online questionnaires via mobile devices, the aggregate of this feedback can be compared to other successful projects and used to identify improvements that can be made in-flight. Big Data is enabling the real-time adjustment of projects and contingency planning based on data derived from multiple sources that help to provide industry best practices.

Can we not, therefore, apply similar principles to improve IT project delivery, and specifically digital transformation projects?

Some of these principles already are being used, at least in part. For example, document management, project management, and social collaboration tools hosted and shared in the cloud provide greater flexibility for the project team, partners, and suppliers; or the use of analytics to both identify areas for business improvement and track the post-implementation results of the project aimed to deliver these improvements.

However, we can also use industry data on project delivery to aid us in identifying what is the best candidate project for digital transformation, driving innovation into the areas that will benefit most; or evaluating the status of delivery based upon other equivalent projects so that remedial action can be taken not only reactively, but proactively.

Consider, for instance, the same data used to support market segmentation could be used to accurately identify and prioritise the processes which would most benefit from change. However, taking a big data approach to patterns and trends from multiple data sources covering customer insight, preferences and buying behaviour, the cost to serve these customers to deliver the products or services, and their demographic, life stage, location and so forth, enables segmentation to be more specific. Similarly, this data would enable processes and transformation strategy to be tailored accordingly, targeting change to the areas that will benefit the most.

Furthermore, changes can be piloted, their effectiveness assessed quickly, and the expected ROI of deployment forecast more accurately. Similarly, changes which would have limited impact on ROI could be avoided. This approach would provide the supporting evidence for targeting a particular transformation and aid the dialogue between consultant and customer as they seek to shape scope and strategy.

This same approach could be used to identify the root cause of issues as well. For instance, targeting a specific customer service issue affecting just those using a particular channel and product, derived from multiple data sources such as customer feedback via social media, analytics on cost to serve, number of product returns, customer demographic, and CSAT reports.

However, it could also be used to identify or even proactively prevent issues occurring in the project delivery itself. In the same way that CII have developed tools for the construction industry to compare disparate project data sources and derive benchmarks and best practices for planning, the same approach could be taken for the IT industry also. Multiple sources of data are already generated by projects during the planning, execution, and post-deployment phases.

By applying a big data approach to both the quantifiable and subjective data from other projects of a similar nature, patterns and trends can be identified. Such data may include project timescales, budget and scope, industry focus, processes being addressed, the technology being deployed, the business units or geographies being supported, and even, as suggested above, assessing the team or stakeholder dynamics. In doing this common delivery issues can be planned for or even avoided. This will increase the probability of a successful project, reduce risk and cost, and potentially deliver faster.

There are other digital innovations which could aid successful project delivery. If the construction industry can have real-time updates on the status of physical assets, then why not also provide real-time push notifications to mobile devices of the status of design or code that is being checked-in or out, and when it is completed? If there are dependencies on the completion, then notifications can be shared accordingly, perhaps integrated with a social collaboration tool. For projects working across multiple geographies and time zones, as well as with partners or sub-contractors, fast and effective communication is vital. By potentially eliminating any delay in sharing status changes, any time currently lost in awaiting email updates or project management direction can be minimised.

The potential exists, although it could become overwhelming. The project manager requires a way to assess and respond to all this insight simply and efficiently, or it may prove somewhat counter-productive! There is a way that another form of digital innovation could facilitate this as well.

Intelligent Virtual Agents (IVA’s) built using Natural Language Interaction are becoming increasingly prevalent on customer and agent web portals to facilitate the user through a particular transaction and make recommendations for next steps. These may be to suggest additional products or services to a customer, or proactively recommending specific processes that may be required to be completed in the case of an insurance underwriter using multiple systems for example.

A similar approach could be taken for project managers, by using an IVA built upon an aggregated set of data from multiple sources, suggesting next steps, risks that should be considered, resources that could be rotated, new areas for focus, or innovation that would provide greater ROI or performance improvement. The IVA would facilitate faster and more informed decision-making by making best use of the ‘Big Data’ analytics provided to deliver a successful project.

In this way, we can plan more effectively, tailor scope with greater focus and confidence of success, and minimise the potential for investing time, effort and money in areas which don’t require it. We can improve the efficiency of planning and decision making, communicate across the team faster and more dynamically, and reduce risk and cost.

Essentially we can use digital transformation to mould our digital transformations, make better digital implementation decisions and transform our delivery through digital innovation.

[1] CSAT / NPS – Customer Satisfaction / Net Promoter Score

[2] ESAT / EPS – Employee Satisfaction / Employee Promoter Score

[3] ROI / TCO – Return on Investment / Total Cost of Ownership

[4] CRM / CEM – Customer Relationship Management / Customer Experience Management

[5] HCM / SCM – Human Capital Management / Supply Chain Management

References

Anthony (Tony) Bryda. (2015, October 19). Proven Solutions. Retrieved from Linked In: https://www.linkedin.com/pulse/using-big-data-proactive-analysis-improve-capital-project-bryda

Rijmenam, Mark van. (2015, October 08). Big Data Can Help Construction Companies Deliver Projects On Time. Retrieved from Datafloq: https://datafloq.com/read/big-data-construction-companies-deliver-projects-t/143