An IDC InfoBrief, Sponsored by Micro Focus, Vertica and HPE | July 2020

THE VALUE OF ACTIONABLE DECISION SUPPORT AT SCALE

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IDC Infobrief | The Value of Actionable Decision Support at Scale

Sponsored by Micro Focus, Vertica and HPE | Page 2

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Many enterprises are saddled with legacy IT architecture that perpetuates data silos, decision silos, and knowledge silos.

The Struggle with Data Change and Complexity

Data engineers on average work with 9 unique data sources and 8 unique targets per pipeline

BIGGEST CHALLENGE CITED BY DATA PROFESSIONALS:

TOO MUCH DATA

1/3rd of CXOs complain of siloed data

THE 2 BIGGEST CHALLENGES OF U.S. CXOS: LACK OF NECESSARY TECHNOLOGY LACK OF APPROPRIATE ANALYTICS SKILLS

1

2

IDC Infobrief | The Value of Actionable Decision Support at Scale

Sponsored by Micro Focus, Vertica and HPE | Page 3

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Changes typical enterprises experience today:• Exposing previously dark or

dormant internal data

• Procuring more external data (or subscribing to data-as-a-service offerings)

• Using new data types such as image or video or spatial data

• Extending use of descriptive analytics via more diagnostic, predictive, and prescriptive analytics (much based on machine learning or other forms of AI)

Multifaceted Data Like Never Before

What Data and Analytics Changes Are Affecting Enterprises?

n = 310

Source: IDC’s Business Intelligence and Analytics Survey, February 2020

New External

Data

40% 45% 45% 47%

38%55%30%

New Internal

Data

Major Arch

Change

New Cloud BI Tools

NewKPIs

New Data

Types

New Analytics

IDC Infobrief | The Value of Actionable Decision Support at Scale

Sponsored by Micro Focus, Vertica and HPE | Page 4

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About 50% of respondents cite that executives’ decisions are influenced by analytics to a great extent, but this percentage drops to just over 1/3 of frontline workers.Frontline workers’ decisions in the field related to customers or equipment or operations or finances should also be influenced by analytics to a much greater extent.

To achieve this, enterprises should develop plans to operationalize delivery of insights (developed by data scientists or business analysts) via tools and applications used by frontline employees.

These insights can be recommendations delivered at decision time, embedded into enterprise apps that run on the cloud, on premises, and at the edge.

The Challenge of Delivering Insights at Scale

34.8

34.2

19.718.7

17.7

10.67.7 11.0

38.4

46.8

38.1

39.423.2

48.1

0.61.61.02.6

3.52.3

Frontline workers

Knowledge workers

Managers Executives

% o

f res

pond

ents

n 1 = (to no extent) n 2 n 3 n 4 n 5 = (to a great extent)

n = 310

Source: IDC’s Business Intelligence and Analytics Survey, February 2020

Influence of Analytics on ActionsQ. To what extent does the output of BIA influence or affect decision making by each of the following groups?

IDC Infobrief | The Value of Actionable Decision Support at Scale

Sponsored by Micro Focus, Vertica and HPE | Page 5

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Instead of trying to identify and define each business use case of decision making, IT should focus on identifying decision-making patterns — that is, categories of decision types.

Enterprises should create a taxonomy that includes three categories and six subcategories of decision-making patterns.

Categorizing Decision-Support Needs

Decision-Making Usage Patterns

Source: IDC, 2019

Data exploration and investigation

Key driver identification

Continuous planning and forecasting

Conditional decision

automation

Guided root cause analysis

Situational awareness

Algorithmic decision

automation

Enterprise performance management

Decision automation

IDC Infobrief | The Value of Actionable Decision Support at Scale

Sponsored by Micro Focus, Vertica and HPE | Page 6

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Decision-Making Characteristics

Source: IDC, 2019

Look at Decision-Making Characteristics, Not Just PersonasPersonas do determine data access rights and security considerations, but they shouldn’t determine decision-making processes, or the technologies needed to support them.

Avoid considering AI technology or predictive analytics as functionality needed only by data scientists or planning capabilities as only relevant to “planners” or C-level executives.

IDC recommends asking end users about the five decision-making characteristics shown here.

Usage patterns

Conditional decisio

n automation

Algorythmic decision automatio

n

Key driv

er identifi

catio

n

Guided root cause analysis

Continuous p

lanning and forecastin

g

Situatio

nal awareness

LOW HIGH

Scope

Latency

Variability

Ambiguity

Risk

Dec

isio

n va

riabl

es

IDC Infobrief | The Value of Actionable Decision Support at Scale

Sponsored by Micro Focus, Vertica and HPE | Page 7

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• Minimization of data movement (Whenever possible, such a technology solution must minimize or eliminate the need to move data by ensuring an appropriate balance of distributed [at the edge] and centralized [in the cloud and on-premises datacenter] data, analytics, and AI processing resources.)

• “Out of the box” or prebuilt support for commonly used analytics, including support for AI/ML algorithms

• Ability to extend analytic capabilities with customized and unique algorithms using the data scientists’ preferred languages and tools

• Availability of cloud storage APIs (e.g., AWS S3 and S3-Compatible Storage)

• Support for and integration between relational data warehousing and non-relational analytic data management, including open source Hadoop and commercial Hadoop distributions

• Support for standard development languages and skills (e.g., SQL, Java, C++, Python, and R)

• Support for real-time service-level agreements

• Separation of compute and storage to enable flexibility in matching technology resources and costs to variability in analytic workloads

• Support for big data processing requirements, including terabytes per second ingest/egest rate and exabyte storage capacity

Platform “Must Haves” for Decision Support at ScaleA modern data, analytics, and AI architecture requires a cloud-native, services-centric approach that recognizes the need for a range of data processing engines depending on use cases.

“Must-have” capabilities of such a platform include the following:

IDC Infobrief | The Value of Actionable Decision Support at Scale

Sponsored by Micro Focus, Vertica and HPE | Page 8

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Considering Micro Focus, Vertica and HPE› The architecture to address the requirements described earlier must encompass optimized software and

infrastructure.

› Hewlett Packard Enterprise (HPE) and Micro Focus provide one such solution “package.“

› A 30-year partnership between HPE and Micro Focus has produced a joint offering that centers around Vertica, a highly scalable columnar relational analytic database and data warehouse, deployed on HPE infrastructure on the cloud, in the on-premises core, and at the edge.

› The solutions built on the combined Vertica and HPE analytics offering are frequently deployed to support AI/ML and IoT use cases and to support a broad range of other complex questions across data sources and data types.

IDC Infobrief | The Value of Actionable Decision Support at Scale

Sponsored by Micro Focus, Vertica and HPE | Page 9

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Vendor Selection Considerations› Like all companies in the analytics and data technology markets, Micro Focus and HPE face competition.

As always, IDC recommends all clients to go through a thorough technology evaluation process that may include third-party references and/or proofs of concept.

› Evaluate closely the integration points of the joint solution with external data sources and downstream analytic tools and applications.

› In addition, enterprises should evaluate the fit to the Vertica and HPE analytic solution based on use case patterns previously described.

IDC Infobrief | The Value of Actionable Decision Support at Scale

Sponsored by Micro Focus, Vertica and HPE | Page 10

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Next Steps Toward Decision Support at Scale› Rethink what it means to have enterprise intelligence. It can no longer be simply about the production of

reports to be delivered to a few high-level decision makers. Enterprise intelligence must be viewed as a foundational element of the enterprise culture.

› Develop a long-term data and analytics strategy that considers various decision-making patterns.

› Consider IT partners that provide a modern data, analytics, and AI platform that is extensible and leverages a broad partner ecosystem as no single vendor can do it all. This criterion will lead you to solutions that combine the best of open source and commercial technology.

› Don’t expect a single technology to address all requirements. One size does not fit all. SQL-based columnar MPP analytic databases have a role, as do Hadoop-based non-relational data repositories, streaming data processing tools, and a range of upstream and downstream data integration and business intelligence tools.

› Select appropriate data and analytics technology with the most compute power and storage capacity (and flexibility), but also consider security, support from the solution provider, and overall total cost of ownership.

Message from the Sponsors

Vertica is the unified analytics warehouse, based on a massively scalable architecture with the broadest set of analytical functions and end-to-end in-database machine learning. Vertica enables leading data-driven organizations to easily apply these powerful capabilities to the largest and most demanding analytical workloads, arming you and your customers with predictive business insights faster than any data warehouse in the market. Vertica provides a unified analytics platform across major public clouds and on-premises data centers and integrates data in cloud object storage and HDFS without forcing you to move any of your data. For more information: visit vertica.com

Micro Focus specializes in finding and protecting sensitive data, detecting advanced threats, and helping customers adapt and evolve their security posture for the future. For more information: visit microfocus.com.

HPE is a global, edge-to-cloud Platform-as-a-Service company built to transform your business. How? By helping you connect, protect, analyze, and act on all your data and applications wherever they live, from edge to cloud, so you can turn insights into outcomes at the speed required to thrive in today’s complex world. For more information: visit hpe.com