The Automated Partition Management for Analysis Services Tabular Models whitepaper is available here. It describes how to use the AsPartitionProcessing TOM code sample with minimal code changes.
Tag Archives: whitepaper
[Whitepaper] The right RFP response tech and tactics to closing business with D365
![crmnav [Whitepaper] The right RFP response tech and tactics to closing business with D365](https://www.crmsoftwareblog.com/wp-content/uploads/crmnav.jpg "[Whitepaper] The right RFP response tech and tactics to closing business with D365")
Can you think of a more critical step in your sales process than getting a customer to sign a quote? (If so, please tell us.)
How are you currently using the power of D365 in this critical part of your sales process, to help move someone from “proposal sent” to “proposal signed?”
Most businesses are using D365 to capture and store prospect and customer information, but when it comes time for building a quote, they typically import a spreadsheet or Word doc and pass that on to their customer. But…
There’s a better way to create and send sales quotes in D365.
. You’ll learn what to say — and how and when to say it — along with what technology you can and should be using together with Dynamics to ensure every sales proposal you send has a better chance of being signed.
3 ways to close more business using Dynamics, CPQ, and a whitepaper on RFP responses
Closing more business means improving your sales proposal process, and here are three ways to help make that happen:
- Integrate a configure, price, quote solution into your CRM ( for the win!)
- Populate that solution with branded, vertically targeted templates (Look sharp, work smart!)
- Write good RFP responses (yep: good!) with help you’ll find .
CRM & CPQ integration: Tiger Woods doesn’t work on his tennis game
As you likely know, Dynamics CRM does a ton of great stuff: manages sales workflows, customer segmentation, and don’t get me started on the LinkedIn Sales Navigator Application Platform (a.k.a., SNAP).
But when it comes to sales proposal automation, most B2B sales reps will tell you that they often work outside Dynamics. It’s not that the system can’t send proposals, but let’s just say it’s not a rock star in doing so. And that’s ok because:
- that’s not Dynamics’ core functionality, and
- there’s an app for that (CPQ).
Let your tech play to its strengths: use Dynamics to manage prospect and customer relationships, and use a CPQ application (integrated with Dynamics, of course) to create, send, and track your sales quotes.
There’s a reason Tiger Woods (or, as of this moment, Dustin Johnson) doesn’t work on his tennis game: he works on his strengths as that’s where the ROI is.
Better be on-brand and on-point
Think of the last sales proposal you sent or received. Was there anything memorable about it, or was it more like a Word doc?
B2B sales is crazy competitive, and if that proposal didn’t stand out in a crowd, it was forgotten. (And, as a great man once said in slightly different and more profane words, you can .)
After choosing , populate it with proposal templates that both build your brand and target the markets you serve.
Brand building in a sales proposal is primarily about having a professional look and feel. If you’re like most sales people, design is not your forte, which is why a professional designer is definitely a smart investment. Spend a couple hundred dollars, get a dozen templates, and you’re basically good to go:
- you choose a template targeted to the vertical market of the customer,
- populate it with product and pricing configurations (your CPQ vendor should be able to help set this up: , anyway),
- and then you only need to write it, send it, and track it.
Worried about the writing part?
Today, in what we’ll call The Gilded Age of Acronyms and Emojis, writing well has become a lost art. Heck, most of us can’t even write good anymore. But the truth is that it’s often that helps close a sale.
If you’re in the Dynamics business, you know that sales cycles can go months or longer. This means the prospect has plenty of time to truly dig into your proposal. They don’t simply skip to the products and pricing: they take a deep dive. And if there’s a typo or a grammatical error or just terrible, self-centered writing, you can bet that it will be noticed.
Help is available via this . Because while most every sales rep is a solid communicator in person and on the phone, we could all use a little help with the written word.
Thanks for reading all the way to the end! If you have questions about adding CPQ to Dynamics, or improving RFP responses, drop me a line.
Power BI Premium Deployment and Management Whitepaper added!
A new whitepaper authored by Peter Myers presents a comprehensive body of knowledge covering all aspects of deploying, scaling, troubleshooting and managing a Power BI Premium deployment in an enterprise. The whitepaper provides both background information explaiing how various Power BI concepts play together in an enterprise deployment as well as practical examples of challenges encountered in enterprises when scaling Power BI Premium based solutions, and how tools available to Power BI Service Administrators and Capacity Administrators, like the Premium Capacity Metrics app, are used to indicate causes for symptoms witnessed and devise solutions to these challenges
The white paper is available in the Power BI Docs site here
Whitepaper on modeling for AS tabular scalability
This is a guest post from Daniel Rubiolo of the Power BI Customer Advisory Team (PBI CAT).
To scale Tabular models to very big volumes of data, and with the objective of getting as much of the data as possible into memory so users can consume it in their analyses, we must consider data preparation and modeling best practices to help the engine do its best work.
AS (Analysis Services) Tabular at its core has an in-memory columnar database engine optimized for BI exploratory analytics. It’s highly efficient in encoding and compressing data in memory, supporting custom business logic with calculated columns & measures, enabling high concurrency, and delivering blazing fast responses.
In this whitepaper (DOCX, PDF) we cover a simplified overview of how the Tabular engine works, and several data modeling design best practices that take the most advantage of the engine’s inner workings. This article was written with an Azure Analysis Services example, but equally applies to SQL Server 2017 Analysis Services. (With a few exceptions, these guidelines also apply to Excel’s Power Pivot and Power BI Desktop & Service.)
With a real-world example, we cover recommendations such as:
- Benefits of designing your model as a “dimensional model”, a.k.a. “star schema”.
- Pre-processing your data in this manner will enable the most scale for high data volumes, and deliver the best performance at query time.
- Steps for optimizing Dimensions:
- Minimize the number of columns.
- Reduce cardinality (data type conversions).
- Filter out unused dimension values (unless a business scenario requires them).
- Integer Surrogate Keys (SK).
- Ordered by SK (to maximize Value encoding).
- Hint for VALUE encoding on numeric columns.
- Hint for disabling hierarchies on SKs.
- Steps for optimizing Facts:
- Handle early arriving facts. [Facts without corresponding dimension records.]
- Replace dimension IDs with their surrogate keys.
- Reduce cardinality (data type conversions).
- Consider moving calculations to the source (to use in compression evaluations).
- Ordered by less diverse SKs first (to maximize compression).
- Increased Tabular sample size for deciding Encoding, by considering segments and partitions.
- Hint for VALUE encoding on numeric columns.
- Hint for disabling hierarchies.
AS Tabular provides very high flexibility in what models you can build. The guidelines in this article will help you maximize the capabilities you provide with your solutions. We hope you find it useful!
Power BI GDPR Whitepaper is now available
In May 2018, a European privacy law, the General Data Protection Regulation (GDPR), is due to take effect. The GDPR imposes new rules on companies, government agencies, non-profits, and other organizations that offer goods and services to people in the European Union (EU), or that collect and analyze data tied to EU residents. The GDPR applies no matter where you are located.
Today, the Power BI team has released a whitepaper to provide you with some basic understanding of the GDPR and relate that to Power BI. This whitepaper will help you understand options for how to configure Power BI as one part of your overall strategy to meeting the requirements of the GDPR across your organization, which will likely include a variety of different tools, approaches, and requirements.
You can download and review the whitepaper today:
Analyst Whitepaper: How Edge Computing, Serverless, and Machine Learning Will Transform the Enterprise
Machine learning at the edge is becoming an integral part of modern applications. From the web to mobile to IoT, machine learning is powering a new breed of applications through natural user experiences and inbuilt intelligence, all in devices themselves that can make decisions and take actions right on the spot.
Unfortunately, today’s virtual machines and containers are too heavy of an option to be deployed at the edge. As a result, developers are moving towards a serverless compute model which uses code snippets as a unit of execution. This enables machine learning models to be packaged as functions so they can run at the edge.
The convergence of machine learning, edge computing, and serverless will become the backbone of digital transformation in industry verticals such as manufacturing, automobile, healthcare, finance, and insurance in the very near future. And TIBCO is here to help.
TIBCO has built solutions that enable developers to bring the power of machine learning and serverless computing to the edge. We now have edge computing and serverless computing platforms integrated with machine learning frameworks. Our products, like TIBCO Flogo® Enterprise and Project Mashling™, make it easy to develop and deploy intelligent applications packaged as microservices running at the edge.
To learn more about these trends, please download the analyst white paper: “How Edge Computing, Serverless, and Machine Learning Will Transform the Enterprise” today.
New Whitepaper on Planning a Power BI Enterprise Deployment
I’m excited to share that a new technical whitepaper I co-authored with Chris Webb is published. It’s called Planning a Power BI Enterprise Deployment. It was really a fun experience to write something a bit more formal than blog posts. My interest in Power BI lies in how to successfully deploy it, manage it, and what the end-to-end story is especially from the perspective of integration with other data assets in an organization. Power BI has grown to be a huge, wide set of features so we got a little verbose at just over 100 pages.
A huge thank you to Chris Webb for inviting me to be his co-author. Chris is not only whip-smart, but a total pleasure to work with.
Another big thank you to Meagan Longoria for being our tech editor. I like to think of myself as detail-oriented, but I’ve got nothin’ compared to her eagle eye.
We worked primarily with Adam Wilson at Microsoft in terms of getting information, so he deserves a thank you as well for dealing with the questions that Chris and I peppered him with week after week.
I hope you find the whitepaper to to be useful.
Update to Auto-Partitioning Code Sample & Whitepaper
A new version of the whitepaper and code sample of automated partition management (see here for more info) is released! Enhancements include the following.
- Support for 1400 models with M partitions and M expressions. Query partitions (with queries of the data source dialect) are still supported.
- Auto retry n times on error for near-real time scenarios and environments with network reliability issues.
- Integrated authentication for Azure AS with synchronization between on-prem Windows AD and Azure AD.
- Auto max date automatically set to current date option for easier maintenance of configuration data.
- All of the above is configurable in the configuration and logging database, which now uses a SQL Server Database project for easier deployment and schema comparisons of new versions.
- Classification of log messages as Error or Informational.
DirectQuery in SQL Server 2016 Analysis Services whitepaper
I am excited to announce the availability of a new whitepaper called “DirectQuery in SQL Server 2016 Analysis Services”. This whitepaper written by Marco Russo and Alberto Ferrari will take your understanding and knowledge of DirectQuery to the next level so you can make the right decisions in your next project. Although the whitepaper is written for SQL Server Analysis services many of the concepts are shared with Power BI.
A small summary of the whitepaper:
DirectQuery transforms the Microsoft SQL Server Analysis Services Tabular model into a metadata layer on top of an external database. For SQL Server 2016, DirectQuery was redesigned for dramatically improved speed and performance, however, it is also now more complex to understand and implement. There are many tradeoffs to consider when deciding when to use DirectQuery versus in memory mode (VertiPaq). Consider using DirectQuery if you have either a small database that is updated frequently or a large database that would not fit in memory
Download the whitepaper here.
Whitepaper and Code Sample for Automated Partition Management
Analysis Services tabular models can store data in a highly-compressed, in-memory cache for optimized query performance. This provides fast user interactivity over large data sets.
Large data sets normally require table partitioning to accelerate and optimize the data-load process. Partitioning enables incremental loads, increases parallelization, and reduces memory consumption. The Tabular Object Model (TOM) serves as an API to create and manage partitions. TOM was released with SQL Server 2016 and is discussed here. Model Compatibility Level 1200 is required.
The sample,
- Is intended to be generic and configuration driven.
- Works for both Azure Analysis Services and SQL Server Analysis Services tabular models.
- Can be leveraged in many ways including from an SSIS script task, Azure Functions and others.
Thanks to Marco Russo (SQLBI) and Bill Anton (Opifex Solutions) for their contributions to the whitepaper and code sample.