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How Digital Disrupts Operations, Business Processes And Customer Experience


 

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Tom Cole, CEO at RDA comments on an article from Forbes – "If you are a member of the "C-suite" or executive team and have time to read just one article on why your business needs digital transformation - this is that article.”


Digital technology is improving enterprise performance in game-changing ways. According to a recent Harvard Business School report, digitalization—the integrated use of analytics, big data, the cloud, the Internet of Things (“IoT”), mobile, and application development—is driving change at unprecedented rates. The report states: “Our digital economy is subject to Moore’s law and digital transformation has become the new normal.” Marco Iansiti & Karim R. Lakhani, The Digital Business Divide: Analyzing the Operating Impact of Digital Transformation (Harv. Bus. School Report, 2016). No business leader can afford to ignore digital; it is now a matter of survival. Harvard’s empirical studies show that digitally transformed organizations—so-called “digital leaders”—quantifiably outperform their counterpart “digital laggards.” Robert Brock, Marco Iansiti & Karim R. Lakhani, What Companies on the Right Side of the Digital Divide Have in Common (Harv. Bus. Rev., Jan. 31, 2017). Enterprises must confront a stark reality: integrated digital technology is changing their customer experiences, operation models, and business models – three critical pillars of digitalization discussed in depth below.

Enterprises cannot realize the benefits of digital transformation without answering at least three prior questions to guide them. First, how can they turn existing legacy systems into a strength that can be leveraged rather than a burden that hinders digitalization? Second, why and how must an enterprise adopt what McKinsey calls a “two-speed IT architecture” for the digital enterprise. Oliver Bossert, Chris Ip, and Juergen Laartz, A Two Speed IT Architecture for the Digital Enterprise (McKinsey & Co., Dec. 2014). And third, why do digital leaders adopt data platforms for application development, the heart of digital transformation? I address these three questions at a business level in the context of the three digital pillars, with future resources to follow.

I.    What is Digital Transformation?

Digital transformation is the manner in which enterprises apply digital technology to their business and operations processes, as well as customer interactions, thereby experiencing changes in their insights into and interactions with customers, as well as to the aforementioned models. Becoming a digital business means applying technology to enable new types of products and processes rather than simply enhancing existing processes and modes of interaction. Digital strategy depends on the use of digital assets in new ways. These include analytics (predictive and prescriptive); big data; mobile; cloud; the IoT; and application development. Digital transformation cannot be separated from technology, but it also requires a culture that encourages the enterprise to change in real time with a business landscape that changes constantly. This is not easy, and often involves a good deal of catching up to digital leaders.

Most enterprises are far from transforming digitally despite acknowledging the effect it will have on revenue in 5-10 years. In this respect, it is important to remember that not all enterprises were (or even are now) “born digital” with all the advantages that status entails, such as remaining unencumbered by disparate, non-integrated technology solutions. This does not mean that traditional brick-and-mortar companies cannot become business leaders. They do. But they must invest in the process and work harder to keep pace with innovation. According to McKinsey & Co., bridging the digital gap requires

“Having a more active digital agenda than others, . . . attracting and retaining digital talent . . . and taking more risks in their digital programs, moving faster to implement initiatives and reallocating resources and their best people to digital work.”

Cracking The Digital Code (McKinsey & Co. Global Survey Results, 2015).

Yet many companies are doing precisely the opposite. They “continue to build ever-more complicated IT systems, deploying new features or patches and fixes on the fly to meet immediate needs without any clear road map or consideration of future IT needs.” Id. It’s critical to emphasize that existing “old” systems are by no means irrelevant; rather, they must be leveraged to play an important part in building out new digital capabilities.

The irony of this misguided development is that it assumes falsely that existing investments, including legacy systems, cannot be leveraged on a successful digital path. On the contrary, enterprises must leverage and extend existing systems. Forrester analysts John Rymer and Liz Herbert call this application model “Hybrid Extend”. John R. Rymer & Liz Herbert, Mantra for Customer-Obsessed Software Leaders: Deliver More, Develop Less (Forrester, Jan. 18 2017). Rather than replace existing operational systems, an enterprise must maximize the utility thereof together with new applications that are integrated through the extension of data models. This allows for greater systems harmonization, as well as refactoring of existing work to support new digital models.

II.    Integrating Business With IT: Overcoming Barriers To Become A Digital Enterprise

Deep alignment of business goals and IT’s efforts to support and supply technology to the enterprise is paramount to digital transformation. Enterprises that successfully achieve this have become digitally mature and relevant. Tension between developers’ need for speed and operations’ need for control is a hallmark of modern business. The combined effort of Operations and Development (DevOps) to gain continuous integration and continuous delivery (CICD) solutions are central to achieving the velocity of innovation required to be competitive in the digital world. These terms refer to the collaboration of business-driven developers with IT, enabled by automated tools for software delivery and deployment. This provides a culture and environment where building, testing, and releasing software happens quickly and frequently, and the work of many developers is integrated several times within a given period (often daily) so that each component can be deployed as it is ready and required.

For established companies, including those that are not digital laggards, achieving this measure of agility at scale is no small feat. It is made difficult by the mismatch of existing legacy systems’ cadence and new systems that require different development methodologies; organizational structures; and multi-tiered existing business processes. Each is directly related to a pillar of digitalization. What is needed is a two-speed architecture that bridges both legacy and digital systems so that each can function optimally within its respective development cycle methodology, while still functioning coherently across the enterprise. This allows enterprises to leverage all capabilities in its legacy systems to fully support newer digital business.

The most critical technology needed for this two-speed architecture to propel digital transformation is a digital platform that integrates legacy-based data with intelligence gathered from digital initiatives. According to a joint report by the MIT Center for Digital Business and Capgemini Consulting, data integration remains the biggest challenge to digital transformation. See Digital Transformation: A Roadmap For Billion-Dollar Organizations (MIT Sloan Management & Capgemini Consulting Joint Report 2015).  

III.    Pillars of Digitalization

Digitalization enables three primary pillars of change. They are: (i) customer experience and engagement; (ii) operational processes; and (iii) business processes. Successful digital transformation is not the result merely of implementing new technologies, but also of transforming enterprise processes to reap increased profit margins and to seize the possibilities for innovation that those process changes make possible.

III.A.    Transforming Customer Experience

Demanding customers have never been so empowered by technology. At the same time, the digital world allows enterprises to meet customer demands in new ways. In supply chain management, for example, world-class analytics allow a parts manufacturer in Kansas City to see in real time and communicate to customers in Hamburg, Germany, the downstream effects of crippled factory floor machinery. Other customer experiences are realized in the realm of new product development where, according to McKinsey, “the ability to offer new products on a timely basis has become an important competitive factor.” Cracking The Digital Code, supra at 2. Companies must become skilled at digital product innovation that meets changing customer demands. Highly advanced retailers rely on analytics-based real-time and historical data to shuffle merchandise delivery in order to meet demand as efficiently as possible, including favoring partitioning available supply to those customers who are known to be most sensitive to delivery times and supply shortfalls. Your behavior and preferences matter, and you are being watched closely.

III.B.    Transforming Operational Processes

Technology alone cannot solve the problems that require two-speed agility. Rather, organizations need to simplify their operations, identify bottlenecks in those processes, and minimize work in process by deploying as often as possible. Companies are offering new digitally enabled services based on IoT monitoring of their products, both in consumer and B2B situations. Rather than the customer contacting the company upon failure of a product; the company is alerted based upon real-time data analysis that a proactive visit from a services person is in order; a part is replaced prior to its failing; and services are scheduled by the customer at a time that works for them.

III.C.    Transforming Business Models

Digitalization transforms business models in two highly related ways. The first is to truly globalize operations, thereby making an enterprise “digitally global” and allowing us to measure its global digital footprint. In a world where domestic U.S. distributors can sell specialty English tea as easily as (and for less than) British manufacturers can do so directly. This global digital presence allows a domestic enterprise—for these purposes, American—to remain competitive in a shrinking world. Second, digitalization creates new, network-centric ways an enterprise connects with partners and customers offering new business relationships. According to MIT, this network effect leads to “a combinatorial explosion of business possibilities” that translate into partner and customer benefits. The Digital Business Divide, supra at 8. The value of a business network grows as it expands. This is especially true to any given node (i.e. enterprise) in the network when it uses a database platform that includes all data management solution components and access to much of the database platform’s partner network. The key here is that enterprises can leverage and gain insights not only from their own stored data, but also from that of their partners. This is a business paradigm spurred by data and the manner in which enterprises share it.

IV.    Open Source Database Infrastructure for Digital Transformation

For years, database technology converged around a single model – the relational model. The trend was to gain insight by consolidating all your data into one system. Over the last 10 years, the Web has driven new models such as NoSQL, Big Data (HDFS), and Graph databases. These technologies arose in response to new application models as applications for the Web grew in scale and with higher volumes of data and more granular transactions. These transactions are more so events that need to be captured, but they have a less stringent requirement for consistency. These events, whether clicks on a website tracking what people look at or generated by a device, are not the same sort of transaction as a financial transaction, or one where inventory must be committed. Eventual consistency makes sense with event capture, and allows higher throughput. Databases like MongoDB and Cassandra are being used to good effect for this.

The data generated in event capture is often consolidated in the Hadoop Distributed File System (HDFS) and then analyzed through Map Reduce or Graph database approaches. This analysis is different from reporting on a relational database. It is less ad-hoc, and more a specific set of applications that are written to discover relationships and trends in the data. Big Data is just that, a mechanism for exploring very large sets of very granular data and discovering insights. Examples are trends in failure based on events sent from an instrumented device such as a turbine. In retail, one might discover that people have a preference for purple, and see a lot of events where people bought things that were purple, but looked at other things where no purple was offered, and did not proceed with a purchase.

The new data landscape is composed of event capture data systems; HDFS; and traditional relational ACID transactional databases for capturing and processing orders; as well as other financial transactions, managing inventory, and dealing with payroll. The new data landscape puts new demands on the operational database system. To bridge across to NoSQL content, many of these systems have added new data types such as JSON so that they can consume unstructured data and match it with the structured data systems that support this—and perhaps key-value/attribute data—are often called multi-model systems.

The leading operational databases now support adaptors to allow including data from NoSQL and HDFS into transactions. This allows the operational database to establish relationships across the complete data landscape, or to query back into HDFS to gain in-context insights that have been generated there. Again, an enterprise must leverage its reach across its network of partnerships, not just its own.

The operational database also supports replication to/from both like and unlike databases. One might want to replicate bill-of-materials data from an Oracle ERP system where the data is stored in the Oracle database, or replicate new customers captured from a website back into the ERP system.

A viable data integration platform supports all this and more. This allows customers the freedom to leverage new more cost efficient open source-based solutions for newly developed applications that require the robustness and transactional capabilities of a relational database management system. 

 

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