Real-time Data Governance, Risks and Security Challenges – Part 1: Impact of major shifts in business applications and use-cases

Real-time Data Governance, Risks and Security Challenges – Part 1

Real-time Data Governance, Risks and Security Challenges – Part 1: Impact of major shifts in business applications and use-cases

“The reality is business leaders are moving full speed ahead, with or without you”.

 

The biggest challenges in data governance and security, today, are a result of speed, scale and the variety of data & technologies used to implement emerging business use-cases. Enterprises are developing a multichannel, multi-device strategy of the future. These strategies include elements like mobile first, responsive design, progressive web apps; building for agility and moving towards event-driven architectures, micro-services, and APIs that provide consistency without sacrificing functionality.

Understanding the impact of real-time business applications and use-cases is critical for planning, designing and implementing a data governance, risk management and security strategy of the future. For example, a Digital experience platform (DXP) is emerging as a requirement for digital businesses. DXP represents a rationalized and integrated set of technologies and services used to deliver a variety of digital experiences to a variety of audiences. DXPs are used to create websites, portals, mobile apps, and IoT. A DXP can deliver better results, accelerated time-to-market and perhaps even lower TCO.

Businesses are increasingly operating in a real-time environment as most data used for “decisioning” is produced continuously from various sources such as Mobile Apps, Web Clickstreams, Application Logs, IoT Sensors, and so on. With briefest of passage of time the value of streaming data used in real-time decision-making diminishes or perishes, very quickly. The nature of such data is typically noisy, and has complex schemas and cyclical volumes. Additionally, increasing support for applications like bots and conversational UIs require real time responses based on unstructured data.

 

By 2022, more than 10% of customer engagement hub architectures will include real-time event streaming and streaming analytics.

 

There is a shift away from products to an experiences economy where the customer experience is driven by more sophisticated personas, context (intent, environment, community), content (granularity, variety and a diverse set of sources). There is an expanding set of complications impacting customer experience including external sources of information (various; often unknown quality), analytics (frequency, recency, segmentation, and so on), detected patterns and trends, personalization, and others such as master data, managed enterprise data from BI, analytics, CRM databases and so on. Additionally, there is an expanding set of customer interaction end-points such as mail, POS, web / portal, contact center, mobile, IoT, and others.

Enterprises are embarking on a customer experience strategy by making it a company priority. CX puts the customer at the center of the business focus by understanding and improving their journey while analyzing all interactions. It is being driven by company executives and embraced by all employees within the organization. CX initiatives aim to deliver and optimize Omni-Channel customer experiences by implementing an agile and adaptable technology foundation. The customer’s experience is now a cumulative effect of interactions with an enterprise’s employees, channels, systems or products – storefront, sales associates, channel partners, customer-facing processes, contact centers, websites/portals, mobile and you. However, rigid silos of customer engagement are scattered throughout the enterprise, adversely impacting CX.

There are several emerging initiatives to bridge these silos to deliver Omni-Channel experiences by building bridges across processes, knowledge and data.

Enterprises are exploring the use of customer journey builder tools to bridge process silos and facilitate customer journeys. These tools allow you to map a customer and their journey rather than your company’s org chart and its processes. Essentially letting your customer lead you rather than the other way around. Organizations are also using information management methods and tools such as master data management (MDM) and Integration data hubs to bridge the data gap. The overall business objective here is to create a single, logical or a 360-degree view of the customer profile. This includes both first-party (likely scattered across multiple internal customer databases, data warehouses, data marts, data lakes, etc.) and third-party data obtained from a data management platform (DMP).

Furthermore, customer engagement is shifting away from enabling the last best experience to the best next experience. There is a clear move towards target customers and audience segments with contextual experiences through personalization. Leveraging contextual attributes such as behavior, trends, device, and location (that go well beyond demographics) has become absolutely critical now.  There a shift towards real-time intelligence-driven decisions on what product / ad / messages need to be pushed – right now. These decisions are based on the current context such as what step in the buying process / journey is the customer at or what does the user want at the present moment.

Among emerging applications, there is an emerging shift away from apps and towards conversational platforms. Such platforms underpin VPAs (Virtual Personal Assistants), VCAs (Virtual Customer Assistants) and Bots. They typically include a natural-language processing (NLP) engine and a user interface (UI) that receives the request and delivers the response via speech or text. Additionally, the front-end interface is typically supported with a search and knowledge engine that can traverse big data repositories of knowledge and content. A context engine analyzes the intent of an individual and delivers personalized answers and other actions. These platforms will significantly drive future interactions to buy, sell, negotiate, complain and make deals.

 

By 2019, requests for customer support through consumer messaging apps will exceed requests for customer support through social media.

 

By 2020, 25% (up from 10% in 2015) of customer service and support operations will integrate VCA technology across engagement channels.

 

Another emerging trend is to do with autonomous decision-making and actions, for example, Thing Commerce where, for example, smart things make purchases on behalf of the human customer by directly taking requests from the customer or inferring demand based on the rules, context and customer preferences to make optimized decisions. The primary benefit of Thing Commerce is to reduce customer efforts and friction in purchases.

 

By 2020, 5% of the Digital Commerce Transactions will come from a Thing.

 

In this blog, we explored some of the emerging business applications and use-cases that need to be understood now for developing strategies for addressing the associated governance, risks and security requirements, comprehensively.

In subsequent blogs we will explore the impact of shifts in architectures, operating environments, and emerging integration solutions.

References:

  1. Michael Maoz, Magic Quadrant for the CRM Customer Engagement Center, Gartner Application Strategies & Solutions Summit, 2017.

  2. Olive Huang, The state of customer experience technologies and their impact to your application strategy, Gartner Application Strategies & Solutions Summit, 2017.

  3. Jason Daigler, To the point: Thing commerce: Expand sales and engage customers through smart things, Gartner Application Strategies & Solutions Summit, 2017.

  4. Neil MacDonald, Gartner Security & Risk Management Summit 2017.