Tag Archives: Analytics

Data Blending: Why are Some Metric Values Blank in Documents Using Multiple Datasets in MicroStrategy Analytics Enterprise 9.4.1 (Part 8)

MicroStrategy Analytics Enterprise

Introduction

Starting with MicroStrategy Analytics Enterprise 9.4.1, Report Services documents can contain grids with objects coming from more than one dataset.

Multiple Datasets in a Single Grid/Graph/Widget Object in MicroStrategy Web 9.4 [2]

Users now have the ability to add attributes and/or metrics from multiple datasets to a single grid, graph, or widget. For example, if Dataset #1 contains Category and Revenue and Dataset #2 contains Category and Profit, a grid can be created which contains Category, Revenue, and Profit.

Part 8 - 1

Administrators can control the use of multiple datasets in a single grid, graph, or widget through the Analytical Engine VLDB properties window at the project level.

  1. Right mouse click (RMC) on the project name.
  2. Select Project Configuration.
  3. Click on Project Definition.
  4. Select ‘Advanced’.
  5. Click “Configure” under the Analytical engine VLDB.

Part 8 - 2

NOTE:

The default value is set to: “Objects in document grids must come from the grid’s source dataset only”.

Users can set the set the source of the grid to a particular dataset or choose no dataset (in which case, the MicroStrategy engine will determine the best suited dataset). [1]

The MicroStrategy Analytical Engine displays no data for metrics in ambiguous cases or when there is a conflict. Ambiguous cases can arise in cases where multiple datasets contain the same objects.  Examples based on the MicroStrategy Tutorial project have been provided to explain this information.

Note: When the MicroStrategy Analytical Engine cannot resolve the correct datatset as explained in the cases below, the data displayed for these will correspond to the value chosen for the missing object display under Project Configuration > Report definition > Null values > Missing Object Display. The default value for this blank.

Case1:

Multiple datasets have the same metric. Only one dataset does not contain this metric and this dataset is set as the source of the grid.

This case is explained with an example based on the MicroStrategy Tutorial project.

1. Create the following objects:

a. Dataset DS1 with the attribute ‘Year’ and metric ‘Profit’.

b. Dataset DS2 with the attribute ‘Year’ and metrics ‘Profit’, ‘Revenue’.

c. Dataset DS3 with the attribute ‘Quarter’ and metric ‘Cost’.

2.  Create a document based on the above datasets and create a grid object on the document with the following objects: ‘Year’, ‘Quarter’, ‘Profit’. Set the source of this grid to be the dataset ‘DS3’.

3. In the executed document, no data is displayed for the metric ‘Profit’ as shown below.

Part 8 - 3

In the above example, the metric ‘Profit’ does not exist in the source dataset ‘DS3’ and exists in more than dataset which are in the document i.e., it exists in both ‘DS1’ and ‘DS2’. Since the engine cannot just randomly pick one of the two available datasets, it chooses not to display any data for this metric. If users do not want such blank columns to be displayed, set the source dataset so that such ambiguity does not arise.

Case 2:

The same metric exists multiple times on the grid. For example, users can have a smart compound metric and a component metric of this compound smart metric on the grid in the document. The smart metric and the component metric are from different datasets.

This case is also explained with an example based on the MicroStrategy Tutorial project.

1. Create the following objects:

a. Dataset DS1 with attribute ‘Year’ and metric ‘Profit’.

b. Dataset DS2 with attribute ‘Year’ and metrics ‘Revenue’, ‘Profit’, ‘Profit Margin’ (this is a compound smart metric calculated from metrics Revenue and Profit).

2. Create a document based on the above datasets and create a grid object on the document with the following objects: ‘Year’, ‘Revenue’, ‘Profit’ and ‘Profit Margin’. The source of this grid object is set to DS1.

3. In the executed document, no data is displayed for the metric ‘Profit Margin’, as shown below.

Part 8 - 4

In the above example, since the source of the dataset is set to ‘DS1’, the ‘Profit’ metric is sourced from this dataset and the metric ‘Revenue’ is sourced from the dataset ‘DS2’ (as this is the ONLY datatset with this metric). However, for the metric ‘Profit Margin’, the component metric ‘Profit’ exists on dataset ‘DS1’, so this becomes a conflict metric and is not displayed. If the source of the grid is changed to ‘DS2’, the data is displayed correctly as shown below.

Part 8 - 5

References:

[1] MicroStrategy Knowledgebase, Why are some metric values blank in documents using multiple datasets in MicroStrategy Analytics Enterprise 9.4.1, TN Key: 44517, 12/16/2013, https://resource.microstrategy.com/support/mainsearch.aspx.

[2] MicroStrategy Knowledgebase, Multiple datasets in a single grid/graph/widget object in MicroStrategy Web 9.4, TN Key: 44944, 09/30/2013, https://resource.microstrategy.com/support/mainsearch.aspx.

NOTE: You may need to register to view MicroStrategy’s Knowledgebase.

Gartner Releases 2014 Magic Quadrant for BI and Analytics Platforms

Gartner Magic Quadrant BI 2014

Readers:

Gartner has just released its 2014 Magic Quadrant for Business Intelligence and Analytics Platforms.

I need a few days to soak this in and better comment on it. But, for now, I thought I would share the Magic Quadrant with you.

You can see the entire report by clicking here.

Best regards,

Michael

Gartner describes and defines the market as follows.

The BI and analytics platform market is in the middle of an accelerated transformation from BI systems used primarily for measurement and reporting to those that also support analysis, prediction, forecasting and optimization. Because of the growing importance of advanced analytics for descriptive, prescriptive and predictive modeling, forecasting, simulation and optimization (see “Extend Your Portfolio of Analytics Capabilities”) in the BI and information management applications and infrastructure that companies are building — often with different buyers driving purchasing and different vendors offering solutions — this year Gartner has also published a Magic Quadrant exclusively on predictive and prescriptive analytics platforms (see Note 1). Vendors offering both sets of capabilities are featured in both Magic Quadrants.

The BI platform market is forecast to have grown into a $14.1 billion market in 2013, largely through companies investing in IT-led consolidation projects to standardize on IT-centric BI platforms for large-scale systems-of-record reporting (see “Forecast: Enterprise Software Markets, Worldwide, 2010-2017, 3Q13 Update”). These have tended to be highly governed and centralized, where IT production reports were pushed out to inform a broad array of information consumers and analysts. While analytical capabilities were deployed, such as parameterized reports, online analytical processing (OLAP) and ad hoc query, they were never fully embraced by the majority of business users, managers and analysts, primarily because most considered these too difficult to use for many analytical use cases. As a result, and continuing a five-year trend, these installed platforms are routinely being complemented, and in 2013 were increasingly displaced, in new sales situations by new investments, and requirements were more skewed toward business-user-driven data discovery techniques to make analytics beyond traditional reporting more accessible and pervasive to a broader range of users and use cases.

Also in support of wider adoption, companies and independent software vendors are increasingly embedding both traditional reporting, dashboards and interactive analysis, in addition to more advanced and prescriptive analytics built from statistical functions and algorithms available within the BI platform into business processes or applications. The intent is to expand the use of analytics to a broad range of consumers and nontraditional BI users, increasingly on mobile devices. Moreover, companies are increasingly building analytics applications, leveraging new data types and new types of analysis, such as location intelligence and analytics on multistructured data stored in NoSQL data repositories.

PRIME: MicroStrategy Announces Release of Cloud Based, In-Memory Analytics Service, Running at Multi-Terabyte Scale

MicroStrategy Cloud’s New Parallel Relational In-Memory Engine (PRIME) Provides High Performance On Big Data Allowing Companies to Build High-Scale, Easy-to-Use Information Driven Apps

Las Vegas, NV, January 28, 2014 – MicroStrategy® Incorporated (Nasdaq: MSTR), a leading worldwide provider of enterprise software platforms, today announced the availability of its new Parallel Relational In-Memory Engine (PRIME) option for the MicroStrategy Cloud™ at its annual user conference, MicroStrategy World 2014, in Las Vegas. MicroStrategy PRIME™ is a massively scalable, cloud-based, in-memory analytics service designed to deliver extremely high performance for complex analytical applications that have the largest data sets and highest user concurrency. Facebook has successfully built high value information-driven applications with the technology that powers MicroStrategy PRIME.

“Rising data volumes are fueling demand for compelling, easy-to-use analytical applications with the power to revolutionize existing business processes for thousands or tens of thousands of employees, customers, or partners,” said Michael Saylor, CEO, MicroStrategy Incorporated. “MicroStrategy PRIME has been built from the ground up to support the engineering challenges associated with development of these powerful new information-driven apps. This innovative service will allow organizations to derive maximum value from their information by making their Big Data assets actionable.”

Most organizations struggle to harness the value of the information in their Big Data stores due to poor performance. Big Data technologies can store large amounts of information, but distributing that information in an interactive manner to thousands of users with existing commercially available technologies is a huge challenge, often resulting in risky, multi-year projects. MicroStrategy PRIME breaks new ground by tightly coupling a state-of-the art visualization and dashboarding engine with an innovative massively parallel in-memory data store. This architecture allows companies to build highly interactive applications that deliver responses to hundreds of thousands of users in a fraction of the time and cost of other approaches. MicroStrategy PRIME acts as a performance accelerator, opening up the data in databases to a much larger user population, driving new demand for information.

MicroStrategy PRIME combines:

  • Massively parallel, distributed, in-memory architecture for extreme scale. MicroStrategy PRIME is built on an in-memory, highly distributed, massively parallel architecture, designed to run on cost effective commodity hardware. Complex analytics problems can be partitioned across hundreds of CPU cores and nodes to achieve unprecedented performance. MicroStrategy has worked closely with leading hardware vendors to take full advantage of today’s multi-core, high memory servers.
  • Tightly integrated dashboard engine for beautiful, easy-to-use applications. MicroStrategy PRIME includes a state-of-the-art dashboard and data exploration engine, built on the MicroStrategy Analytics Platform™. The visualization engine includes hundreds of optimizations designed specifically for the in-memory data store. This engine enables customers to build complete, immersive applications that deliver high-speed response.
  • Cloud-based delivery for rapid deployment. MicroStrategy PRIME is available as a service on MicroStrategy Cloud, MicroStrategy’s world-class Cloud Analytics platform. MicroStrategy Cloud offers a complete service, including the infrastructure, people and processes to enable customers to quickly and easily develop and deploy high-scale, information-driven applications.

About MicroStrategy Incorporated

Founded in 1989, MicroStrategy (Nasdaq: MSTR) is a leading worldwide provider of enterprise software platforms. The Company’s mission is to provide the most flexible, powerful, scalable and user-friendly platforms for analytics, mobile, identity and loyalty, offered either on premises or in the cloud.

The MicroStrategy Analytics Platform™ enables leading organizations to analyze vast amounts of data and distribute actionable business insight throughout the enterprise. Our analytics platform delivers reports and dashboards, and enables users to conduct ad hoc analysis and share their insights anywhere, anytime. MicroStrategy Mobile™ lets organizations rapidly build information-rich applications that combine multimedia, transactions, analytics, and custom workflows. The MicroStrategy Identity Platform™ (branded as MicroStrategy Usher™) provides organizations the ability to develop a secure mobile app for identity and credentials. The MicroStrategy Loyalty Platform™ (branded as MicroStrategy Alert) is a next-generation, mobile customer loyalty and engagement solution. To learn more about MicroStrategy, visit www.microstrategy.com and follow us on Facebook and Twitter.

MicroStrategy, MicroStrategy Analytics Platform, MicroStrategy Mobile, MicroStrategy Identity Platform, MicroStrategy Loyalty Platform, MicroStrategy Usher, MicroStrategy Cloud and MicroStrategy PRIME are either trademarks or registered trademarks of MicroStrategy Incorporated in the United States and certain other countries. Other product and company names mentioned herein may be the trademarks of their respective owners.

Has MicroStrategy Toppled Tableau as the Analytics King?

MicroStrategy Analytics

In a recent TDWI article titled Analysis: MicroStrategy’s Would-Be Analytics King, Stephen Swoyer, who is a technology writer based in Nashville, TN, stated that business intelligence (BI) stalwart MicroStrategy Inc. pulled off arguably the biggest coup at Teradata Corp.’s recent Partners User Group (Partners) conference, announcing a rebranded, reorganized, and — to some extent — revamped product line-up.

One particular announcement drew great interest: MicroStrategy’s free version of its discovery tool — Visual Insight — which it packages as part of a new standalone BI offering: MicroStrategy Analytics Desktop.

With Analytics Desktop, MicroStrategy takes dead aim at insurgent BI offerings from QlikTech Inc., Tibco Spotfire, and — most particularly — Tableau Software Inc.

MicroStrategy rebranded its products into three distinct groups: the MicroStrategy Analytics Platform (consisting of MicroStrategy Analytics Enterprise version 9.4 — an updated version of its v9.3.1 BI suite); MicroStrategy Express (its cloud platform available in both software- and platform-as-a-service  subscription options; and MicroStrategy Analytics Desktop (a single-user, BI discovery solution). MicroStrategy Analytics Enterprise takes a page from Tableau’s book via support for data blendinga technique that Tableau helped to popularize.

“We’re giving the business user the tools to join data in an ad hoc sort of environment, on the fly. That’s a big enhancement for us. The architectural work that we did to make that enhancement work resulted in some big performance improvements [in MicroStrategy Analytics Enterprise]: we improved our query performance for self-service analytics by 40 to 50 percent,” said Kevin Spurway, senior vice president of marketing with MicroStrategy.

Spurway — who, as an interesting aside, has a JD from Harvard Law School — said MicroStrategy implements data blending in much the same way that Tableau does: i.e., by doing it in-memory. Previous versions of MicroStrategy BI employed an interstitial in-memory layer, Spurway said; the performance improvements in MicroStrategy Analytics Enterprise result from shifting to an integrated in-memory design, he explained.

“It’s a function of just our in-memory [implementation]. Primarily it has to do with the way the architecture on our end works: we used to have kind of a middle in-memory layer that we’ve removed.”

Spurway described MicroStrategy Desktop Analytics as a kind of trump card: a standalone, desktop-oriented version of the MicroStrategy BI suite — anchored by its Visual Insight tool and designed to address the BI discovery use case. Desktop Analytics can extract data from any ODBC-compliant data source. Like Enterprise Analytics, it’s powered by an integrated in-memory engine.

In other words: a Tableau-killer.

“That [Visual Insight] product has been out there but has always been kind of locked up in our Enterprise product,” he said, acknowledging that MicroStrategy offered Visual Insight as part of its cloud stack, too. “You had to be a MicroStrategy customer who obviously has implemented the enterprise solution, or you could get it through Express, [which is] great for some people, but not everybody wants a cloud-based solution. With [MicroStrategy Desktop Analytics], you go to our website, download and install it, and you’re off and running — and we’ve made it completely free.”

The company’s strategy is that many users will, as Spurway put it, “need more.” He breaks the broader BI market into two distinct segments — with a distinct, Venn-diagram-like area of overlap.

“There’s a visual analytics market. It’s a hot market, which is primarily being driven by business-user demand. Then there’s the traditional business intelligence market, and that market has been there for 20 years. It’s not growing as quickly, and there’s some overlap between the two,” he explained.

“The BI market is IT-driven. For business users, they need speed, they need better ways to analyze their data than Excel provides; they don’t want impediments, they need quick time to value. The IT organization cares about … things … [such as] traditional reporting [and] information-driven applications. Those are apps that are traditionally delivered at large scale and they have to rely on data that’s trusted, that’s modeled.”

If or when users “need more,” they can “step up” to MicroStrategy’s on-premises (Enterprise Analytics) or cloud (Express) offerings, Spurway pointed out. “The IT organization has to support the business users, but they also need to support the operationalization of analytics,” he argued, citing the goal of embedding analytics into the business process. “That can mean a variety of things. It can mean a very simple report or dashboard that’s being delivered every day to a store manager in a Starbucks. They’re not going to need Visual Insight for something like that — they’re not going to need Tableau. They need something that’s simplified for everyday usage.”

MicroStrategy Analytics Powerful

Something More, Something Else

Many in the industry view self-service visual discovery as the culmination of traditional BI.

One popular narrative holds that QlikTech, Tableau, and Spotfire helped establish and popularize visual discovery as an (insurgent) alternative to traditional BI. Spurway sought to turn this view on its head, however: Visual discovery, he claimed, “is a starting point. It draws you in. The key thing that we bring to the table is the capability to bridge the gap between traditional model, single-version-of-the-truth business intelligence and fast, easy, self-service business analytics.”

In Spurway’s view, the usefulness or efficacy of BI technologies shouldn’t be plotted on a linear time-line, e.g., anchored by greenbar reports on the extreme left and culminating in visual discovery on the far right. Visual discovery doesn’t complete or supplant traditional BI, he argued, and it isn’t inconceivable that QlikTech, Tableau, and Spotfire — much like MicroStrategy and all of the other traditional BI powers that now offer visual discovery tools as part of their BI suite — might augment their products with BI-like accoutrements.

Instead of a culmination, Spurway sees a circle — or, better still, a möbius strip: regardless of where you begin with BI, at some point — in a large enough organization — you’re going to traverse the circle or (as with a möbius strip) come out the other side.

There might be something to this. From the perspective of the typical Tableau enthusiast, for example, the expo floor at last year’s Tableau Customer Conference (TCC), held just outside of Washington, D.C. in early September, probably offered a mix of the familiar, the new, and the plumb off-putting. For example, Tableau users tend to take a dim view of traditional BI, to say nothing of the data integration (DI) or middleware plumbing that’s associated with it: “Just let me work already!” is the familiar cry of the Tableau devotee. However, TCC 2013 played host to several old-guard exhibitors — including IBM Corp., Informatica Corp., SyncSort Inc., and Teradata Corp. — as well as upstart players such as WhereScape Inc. and REST connectivity specialist SnapLogic Inc.

These vendors weren’t just exhibiting, either. As a case in point, Informatica and Tableau teamed up at TCC 2013 to trumpet a new “strategic collaboration.” As part of this accord, Informatica promised to certify its PowerCenter Data Virtualization Edition and Informatica Data Services products for use with Tableau. In an on-site interview, Ash Parikh, senior director of emerging technologies with Informatica, anticipated MicroStrategy’s Spurway by arguing that organizations “need something more.” MicroStrategy’s “something more” is traditional BI reporting and analysis; Informatica’s and Tableau’s is visual analytic discovery.

“Traditional business intelligence alone does not cut it. You need something more. The business user is demanding faster access to information that he wants, but [this] information needs to be trustworthy,” Parikh argued. “This doesn’t mean people who have been doing traditional business intelligence have been doing something wrong; it’s just that they have to complement their existing approaches to business intelligence,” he continued, stressing that Tableau needs to complement — and, to some extent, accommodate — enterprise BI, too.

“From a Tableau customer perspective, Tableau is a leader in self-service business intelligence, but Tableau [the company] is very aware of the fact that if they want to become the standard within an enterprise, the reporting standard, they need to be a trusted source of information,” he said.

Among vendor exhibitors at TCC 2013, this term — “trusted information” or some variation — was a surprisingly common refrain. If Tableau wants to be taken seriously as an enterprisewide player, said Rich Dill, a solutions engineer with SnapLogic, it must be able to accommodate the diversity of enterprise applications, services, and information resources. More to the point, Dill maintained, it must do so in a way that comports with corporate governance and regulatory strictures.

“[Tableau is] starting to get into industries where audit trails are an issue. I’ve seen a lot of financial services and healthcare and insurance businesses here [i.e., at TCC] that have to comply with audit trails, auditability, and logging,” he said. In this context, Dill argued, “If you can’t justify in your document where that number came from, why should I believe it? The data you’re making these decisions on came from these sources, but are these sources trusted?”

Mark Budzinski, vice president and general manager with WhereScape, offered a similar — and, to be sure, similarly self-serving — assessment. Tableau, he argued, has “grown their business by appealing to the frustrated business user who’s hungry for data and analytics anyway they can get it,” he said, citing Tableau’s pioneering use of data blending, which he said “isn’t workable [as a basis for decision-making] across the enterprise. You’re blending data from all of these sources, and before you know it, the problem that the data’s not managed in the proper place starts to rear its ugly head.”

Budzinski’s and WhereScape’s pitch — like those of IBM and Teradata — had a traditional DM angle. “There’s no notion of historical data in these blends and there’s no consistency: you’re embedding business rules at the desktop, [but] who’s to say that this rule is the same as the [rule used by the] guy in the next unit. How do you ensure integrity of the data and [ensure that] the right decisions were made? The only way to do that is in some data warehouse-, data mart-[like] thing.”

Stephen Swoyer can be reached at stephen.swoyer@spinkle.net.