Yelenakibasova
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AMA:预测分析解放军相连nning

您是否想进一步了解预测分析如何使连接计划更容易?亚博vip反水我们最新的问我与德勤的尼克·范德斯型(Nick Vandesype)的任何会议现已开放!发布您的问题!

Anaplan is a perfect enabler to make predictive analytics digestible and consumable. The real value of predictive analytics comes when you can integrate it with a planning solution. Hear more from Nick (@nickvandesype),关于瑞士的主要预测和算法预测,关于预测分析如何使互联计划更加容易。亚博vip反水

How to participate in the Ask Me Anything segment:

1. Watch Nick's Ask Me Anything video on predictive analytics (video goes LIVE on Monday, April 27 at 8 a.m. SCT).

3. Post your questions in the comment section below the video.

4.尼克将整个星期检查页面并回答您的问题!AMA将于5月1日(星期五)下午5点结束。CST。

13 REPLIES13
蒂莫西布嫩
小组负责人 - 员工

缺口,

Thanks for taking the time to do the AMA. Predictive Analytics is an exciting space and Anaplan is certainly a platform that complements and enables the work that Analytics teams are doing within organizations. I'm curious as to how you've seen businesses approach the integration of analytic insights into the planning process within Anaplan, and what challenges you've seen.

Anaplan带来的过程设计比我以前在空间中看到的任何软件都更接近业务用户。它允许许多传统上花费大部分时间在数字处理和数据体操上的人实际参与系统设计的创作过程。这可以创造真正的竞争优势。

With that said, there's often a gap between the technology (analytics) and business process value. Without good design, the analytics get lost. And without good analytics, the decision-making is less informed. Are there any particular best practices you've seen that ensure Analytics are translated effectively into the planning process? With legacy planning systems, UX design was controlled by the software vendor. With Anaplan, businesses are given flexibility and ownership over design. How can organizations take advantage of this to get the most out of their analytics?

nickvandesype
贡献者

Hi@timothybrennan,

感谢您的提问。您提出了一些非常公正的观点。我们通常看到的是,最近几年公司已经登上了数据科学团队,这意味着大多数时候分析技术已经在内部。数据科学家旁边的桌子,计划者/预报员坐在(来自销售,金融,供应,人力资源,...)。计划者定期问:“他数据科学家,你知道未来比我做的更好吗?”过去。策划者问:“很酷,你能把这个客观的视图发送给我。策划者通过类别,产品或SKU获得了一些数字的Excel。

此处的分析并未嵌入到此过程中,两个团队没有对齐。计划者收到了一份手册生成的预测,这是一个黑匣子,对数据科学家如何提出这些数字毫不澄清。他将其用作起点,但会更改输入字段中的数字,因为他认为它们的某种原因或低估了。

您是对的,Anaplan启用了流程设计,这也计算分析过程。在理想的世界中,(在Anaplan中),每天,每周或每月的界面(在Python,r,r,c ++,...)中测量准确性,并定期在Anaplan中更新新见解,为基线,作为基线(客观)起点。该基线应包括内部和外部数据,以最大化基线的准确性。之后,计划者可以进行更改,但应记录(向上或向下)的更改,并应添加原因。在流程结束时,您正在查看“预测增值”(计划者通过更改基线添加的额外精度是什么)。如果这是恒定的正数(这意味着计划者在预测中增加了准确性),则应了解该计划者具有哪些数据或见解,而这些数据或洞察力不包括这些因素并开始包括这些因素。

这就是我们在计划用例中非常成功地嵌入分析的方式。亚搏彩票手机版免费下载

1.提供透明的基线(并将其与分析引擎的自动供稿整合在一起)

2.包括内部和外部数据

3.跟踪更改并了解添加的预测值是

4.优化算法

Jareddolich
Moderator

@nickvandesype

感谢您抽出宝贵的时间来促进AMA。预测分析对我来说确实很重要,因为我是零售业的Anaplan倡导者。

不断出现零售的一个领域正在预测库存。

在您的AMA介绍中,您提到,人们将大量时间用于统计建模(也许是在Anaplan中),有时通过将Anaplan用作协作计划工具而错过了Anaplan的真正优势。我同意这项评估,但这仍然使我们必须计算预测。

Two part question:

  1. 您是否相信我们应该保持统计建模足够简单,以便可以在Anaplan中进行计算,或者在使用R,Python或Knime(例如R,Python或Knime)更适合复杂性时,我们应该在Anaplan之外进行统计建模。
  2. 如果您在Anaplan之外提倡统计建模,那么您对数据集成的经验是什么?关于如何构建Anaplan的任何建议,以便用户可以管理自动化本身(即,再生预测),以便他们可以进行方案计划?

Thank you@nickvandesype呢哦,大喊大叫@YelenaKibasovafor planning this event!


Jared Dolich
nickvandesype
贡献者

Hi@JaredDolich

Thanks for your question.

On the first point: the statistical models in Anaplan are only single regression models, from a statistical point of view, you need a more complex optimizer program to really be able to include multiple regression models. The big difference between both is easy: single regression models can only take 1 explanatory variable into account as in y=ax+b(+e) (which is the actual sales in this case), multiple regression models (the more traditional regression models used in econometrics) can include different explanatory data factors as in y=ax+by+cz+d(+e). The world is too complex to model them as single regression models, so I personally try to avoid these simple regression models. It can work, if you have very seasonalised, lineal growing products - but how often is that the case? Besides that, we also know that 85% of business performance is explained by external data - so excluding external data from predictive solutions is not ideal. So yes, I would go for a python or R solution.

第二点:更详细地在社区页面上描述了不同的可能性:REST API,Anaplan Connect或ETL解决方案(如Informatica)可以帮助将您的Python脚本集成到Anaplan中。知道您始终需要一个位置,服务器来“执行”算法。在我从事的95%的项目中,没有理由对预测模型进行“自我服务”更新。这意味着,如果您将算法的导入和更新一夜安排(可以轻松编程),这就足够了。对于其他5%,我们创建一个链接到服务器的URL,然后单击URL,该过程(来自Anaplan的导出数据,运行算法并将预测发送回Anaplan)。

希望这会有所帮助,如果没有,请告诉我!

ChrisWeiss
社区管理员

@nickvandesype,

感谢您主持这个AMA,这是一个很棒的话题!快速提出问题,您认为哪种类型的预测分析最适合被转移到Anaplan中?不仅集成到互联的计划模型中,而且实际上迁移到在An亚博vip反水aplan中完全计算出来?

谢谢!

-Chris Weiss
nickvandesype
贡献者

嗨,克里斯,

很酷,向您提出问题!谢谢!

我相信,当您谈论预测分析类型时,您会谈论不同的算法?从我的角度来看,这很明显,最使用的时间序列模型从一开始就很有意义。我们可以为Anaplan中的各种用例使用预测分析。在人力资源计划中,在商业领域中,您可以为某个客户/合同设定的最佳价格非常有趣。但是最常用的用例是我们所说的“时间序列”预测分析。这里有一个“时间”元素起着作用(例如,重要的是,该模型必须认识到2月是在2019年1月和2020年之前出现的)。这些模型可以在不同的指标(例如数量,价格,销售,利润率,成本,EBIT,卡车,注射器,FTE,...)中使用。无法定义性能最多的一个,因为每个数据集都不一样,但是将5-10个使用的最常用的数据与Anaplan(如优化器)中的原始集成在一起是有意义的。我正在考虑:Arima,Arimax,多个回归,vector autoregression, time-series gradient boosting, LSTM, Recurrent neural networks, ...

希望这可以帮助!

Piotrweremczuk
Occasional Contributor

尼克

Great to have AMA for Predictive Analytics. I really can't wait to start using it in Anaplan!

Just to expand a little Chris'es question: do you think it makes sense to also enable some sort of scripting language inside Predictive Analytics in Anaplan? I reffer to the options that are available for example in tools like SAS Enterprise Miner or RapidMiner where you can not only use the pre-defined blocks with algorithms that are enabled by default but also can create a custom algorithm using well known programming languages like Python or R.

我要问原因数据科学是一种先进的状态,如果您想实现最佳预测,那么算法的所有属性都必须不可避免和至关重要。但是,从另一方面,我不知道对他们可能对现场预测更感兴趣的业务分析的细节太多了 - 要重新使用数据,通常需要花费很多时间...

期待阅读您的意见。

Fabien.Junod
Certified Master Anaplanner

Thank you@nickvandesype做这个AMA。

If I wanted to go ahead and implement some "simple" predictive analytics, how would I go about it?

- 我需要什么技术知识?

- 我需要编程技能吗?

- Can we understand the drivers of the prediction?

It would be good if you could talk about your experience implementing that with some of your customers.

nickvandesype
贡献者

Hi@fabien.junod,

很高兴听到您渴望从一个简单的预测模型开始。

You can get very easily started, but if you want to grow, some basic knowledge in a coding language (python, R, ...) will be helpful. So yes, some technical know-how would be needed, ideally in one of the two most used data science languages.

理解驱动程序绝对是可能的,但这是可以将一个好的模型与不良模型区分开的地方。为了在预测分析中提供最简单的示例,您可以想象多个回归模型。

You try to predict volumes (y) and you have therefore 4 data points which you want to use: 1) actual volume (a), 2) promotions (b), pipeline/order book (c) and the GDP of the country you operate in (d). If you create a multiple regression model you get something like: y = av + bw + cx + yd + standard error. The drivers' sensitivity is then explained with the factors v, w, x and y). Imagine that x is 0.5. If your 'c' or order book value increases with 1 your volumes (y) will increase with 0.5 (1 times 0.5).

对于所有预测分析模型,这种易于理解的方法都可以计算 - 因此,以正确的方式构建它,您将了解哪些是重要的驱动因素。

Feel free to clarify your question if this answer would not be enough.

谢谢,