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At its core, the phrase “data-driven” means acting based on what the data tells you. Organizations are increasingly adopting data-driven approaches to decision-making. This is natural, given the amount of data we now have on hand.
“数据驱动”的核心意义在于，让决策者根据数据信息来采取行动。越来越多的公司开始采用数据驱动的决策方法。这倒是很自然——考虑到我们手头堆积如山的数据量。
To match the demand, software providers are touting products that claim to facilitate this metric-centric decision making. All-in-all, data-driven is now perceived as the right way to do business. If you’re doing “data-driven decision making”, you’re doing it right.
为了迎合市场这份需求，软件商也正在鼓吹以数据处理为亮点的产品。总之，现在数据驱动被认为是最正确的趋势。如果你在搞“数据驱动的决策”一类的事儿，做什么都是对的。
But are you really?
但真是这样吗？
As we let the data determine more and more actions, we must keep in mind that “data-driven” doesn’t necessarily mean “data accurate.” Nor does it mean “data-efficient” or “data masterful.”
当我们越来越多地通过数据做决定时，我们必须记住，“数据驱动的”并不一定意味着“数据是准确的”。它也不意味着“数据很高效”或“数据很可靠”。
Indeed, I’ve seen data-driven strategies that run the gamut. Some were good, some were bad, and others were just ugly. This article will look into how to set up a good data-driven strategy and how to choose the best predictive analytics solution.
1
Data-driven strategies: The good, the bad and the ugly
数据驱动的策略：好、坏、丑的三类
The good:This kind of data-driven strategy focuses on sourcing more financial and operational data and analyzing it quickly. That’s the magic that happens when you can access to a real data hub that incorporates financial data, but also granular operational data, which are the fire-starter of the revenue, cost, cash generation.
好的那类：这种数据驱动策略侧重于获取更多的财务和运营数据，并快速做出分析。你的数据中枢可以访问到真实的财务数据，又可以获得颗粒度较细的业务数据——显示出收入，成本，现金流等一系列信息。
The bad: This kind of data-driven strategy is incomplete as it lacks vital data sources. It results in inaccurate data that becomes dangerous when used to craft strategies and make decisions. It generally happens when your financial system can sync and merge data from an old, siloed approach, but you still can’t track relevant data or data that the proper granularity.
坏的那类：由于缺乏准确的数据源，这种数据驱动的策略是不完整的。结果是你获取的数据很可能不准确，从而导致决策后的风险。这种情况通常发生在你的财务系统需要从别处同步或合并数据，同时又无法对原始数据的颗粒度进行溯源时发生。
E.g., Retailers who can’t track hourly or day-of sales miss critical information that would inform cost-savings measures, like shift scheduling or sales generation, like in-store promotion.
举个例子：一些零售商如果无法每天或每小时同步销售额，就可能错过用来节约成本的关键信息，比如轮班安排或店内促销。
The ugly:This kind of data-driven strategy can’t get off the ground because its data is uninterpretable. Even if it could, finance would have no way to extract any value from its findings.
丑的那类：由于数据无法被解读，所以数据驱动策略也无从落地。即使可以，财务部门从中得不出什么有用的价值。
If your financial and analysis (FP&A) system can tell you, for example, that you’re going to sell more ice cream if the number of people in the city divided by the average salary of bakers in London is greater than the estimated CPI in three years.
例如，如果你的FP&A团队告诉你，如果伦敦面包师三年下来的薪资涨幅都能跑赢通胀，那咱们的冰淇淋业务的销量肯定有戏。
What do you do next? How do you derive a selling strategy from this accurate yet business agnostic finding that’s pure math? Cool technology, but it lacks any actionable insight.
但你能怎么做呢？你能从这个准确的数据中设计出任何商业策略吗？听起来是挺酷的，但其实它没有任何商业洞察在里头。
To help you determine where you land on the data-driven decision-making spectrum, I’ve made this handy chart to support you on your next steps.
为了帮助大家在数据驱动决策中找到自己的位置，我制作了这个方便的图表来帮助你进入下一步。
2
How do you switch tracks to a good data-driven strategy?
如何切换到数据驱动策略的正确路径上？
I believe the best way to look at this is to understand the path towards an ideal data-driven strategy, boosted by predictive analytics.
我相信看待这一问题的最好方法，是理解“预测分析”如何推动数据驱动策略。
The crème de la crème of data-driven strategies is predictive analytics — specifically predictive analytics with explainable predictions.
数据驱动策略的核心就是在于“预测分析”（predictive analytics）——特别是可解释的预测。
(I’ll explain this concept in a bit.) Predictive analytics produces precise projections that can help shape decisions, guide course corrections, and redirect resources to productive activities.
（我将稍微解释一下这个概念）预测分析产生精确的预测结果，可以用来帮助产生决策，指导路径修正，并在生产活动中重新定向资源配置。
In other words, a conscious and consistent journey towards predictive analytics will put you on the track towards — not just good — but exceptional data-driven decision making.When executed correctly, predictive analytics has the power to leverage all kinds of data and confer predictive power on every financial process.
换句话说，目标明确，步伐连贯的预测性分析将不仅能帮你进入正轨，而且它是特殊的数据驱动决策。如果执行得当时，预测分析可以充分利用各种数据，赋予每个财务过程预测能力。
3
How to choose the best predictive analytics solution to be successful?
如何选择好的预测分析解决方案？
1. Understand the three essential pillars of predictive analytics
1. 理解预测分析的三个基本支柱
In the past, companies used external consultants and data scientists to build and utilize predictive functionality. The burdensome, costly nature of this approach still lingers in finance’s imagination. Yet, times have changed. Although predictive technology has matured beyond recognition, there are several things that a predictive analytics solution must do:
在过去，公司使用外部顾问和数据科学家来构建和运用预测功能。这种方法的负担和成本在财务人的脑海中可谓挥之不去。然而，时代已经改变了。尽管预测技术已经成熟到超出大家过去的认知，但预测分析解决方案必须做到以下几点：
Unify data:Predictive platforms must facilitate a centralized approach to data management. ·
统一过口径的数据：预测平台必须促进集中式的数据管理方法。
Connect operational and financial data:By understanding the connection and interrelations between financial results and operational actions, you can better scrutinize and adjust operational strategy towards a scenario that would produce the optimal financial results. ·
打通运营和财务数据：通过理解财务结果和运营行动之间的联系和关系，你可以更好更细致地审查和调整运营战略，以产生最佳的经济结果。
Be real-time:Access to real-time data is critical to producing precise predictions in times of uncertainty. In-memory computing and a powerful data engine are the two technologies that ensure real-time speed so you can gauge the impacts of unexpected market events or twists and turns in the economy and quickly determine a viable strategic response.·
实时：获取实时数据对于在不确定时期做出准确预测至关重要。内存计算和强大的数据引擎是确保实时的两条保障，这样你就可以判断市场意外事件或经济复杂的影响，并迅速确立可行的战略。
Use explainable predictions
2. 使用可解释的预测
Predictions are only half the battle when it comes to making data-driven decisions. The other half? Understanding what is driving your performance and impacting most the predicted outcomes.
在做出数据驱动的决策时，预测只是任务的一半。另一半是什么？是了解什么在驱动着你的业绩并对预期结果有着最大的影响。
For example, it’s helpful to know a product line’s predicted revenue. But it’s more beneficial to understand that your marketing campaigns and discount policy are the drivers of that revenue. This way, you could invest more in what’s working, less in what’s not and apply your insights to neighboring initiatives.
例如，了解产品线的预期收入是很有帮助的。但更有益的是，你明白营销活动和折扣才是收入的驱动因素。这样你就可以在有效的项目上投入更多，在无效的项目上投入更少，并将你的洞察应用到其他项目上。
Use a suitable predictive analytics software
3.使用合适的预测分析软件
Leveraging a predictive solution without explainable predictions is, in my eyes, like providing a cart without a horse. It lacks a driving force. That’s why it’s important to recognize the main two types of predictive analytics software, as follows:
在我看来，用一个没有可解释性的预测方案，就像坐一辆没有马的马车。它缺乏驱动力。这就是为什么我们必须认识到以下两种类型的预测分析软件：
Black box software: Black box software that gives you predictions but provides no rhyme or reason. You’re expected to trust the machine’s predictions without understanding the correlations it’s made to come to its conclusion.
黑匣子式软件：黑匣子软件提供预测，但不提供任何理由。这类软件商认为你相信机器的预测，而不去理解它得出结论的相关性。
Glass box approach software: This software produces the predictions and spots light on the business drivers responsible for them. This is supporting your savvy data-driven decision-making process because you can take those drivers, change the strategy, and simulate or re-shape the future towards a more fruitful outcome.
玻璃盒式软件：这类软件可以产生预测，并揭示导致这些预测的业务驱动因素。这支持你以精明的数据来驱动决策过程，因为你可以利用这些驱动因素，改变策略，模拟或重塑未来，以获得更有成效的结果。
Don’t treat predictive analytics as a technology. Treat it as a solution
4. 不要把预测分析当成一种技术。把它当作一个解决方案
I’ve seen many organizations fall victim to shiny and new predictive analytics solutions that make data-driven decision-making more of an IT chore than a finance weapon. I suggest that, when you’re vetting a predictive analytics solution or building your requirements for a data-driven strategy, be wary of these artificial intelligence (AI) and machine learning (ML) technology red flags:
我曾见过许多企业成为新预测分析解决方案的受害者，这些解决方案使数据驱动的决策更像是IT工作，而不是财务武器。我建议，当你在研究一个预测分析解决方案或构建数据驱动策略的需求时，要警惕这些人工智能(AI)和机器学习(ML)技术:
Highly tech but difficult to use·
属实是高科技，但是很难使用
AI/ML not integrated with FP&A tools: This leads to auditability problems and time-consuming manual processes. FP&A tools not integrated with ERP: Usually, ERP data models aren’t meant for analysis. They’re meant for transactional processes. It takes a lot of work to unearth ERP data that’s beneficial for planning. Also, in the ERP world, data processes are transactional and fluid which impacts the data quality of snapshots.·
AI/ML没有与FP&A工具打通：这会导致审计方面的问题和过于耗时的手工流程。FP&A工具没有跟ERP打通：通常，ERP数据模型并不用于分析，它们用于业务性流程。从ERP里挖掘做预测时用得上的数据，往往需要大量时间。此外，在ERP领域，数据处理是事物性和极其易变的，这影响了数据质量。
Understand that you can start where you are today
5. 要明白，任何时候开始都不晚
Crawl, walk, run! Don’t let perfect be the enemy of progress. Even as an end goal, predictive analytics becomes a baseline for improved automation, data synthesis, and the drive to underlie more predictive technologies under more processes — so it’s ok to start lean and slow with what you have.
爬也行，走或跑都可以！不要让完美成为进步的敌人。即使是最终目标，预测分析也会成为改进自动化、数据合成的基线，以及在更多流程下为更多预测技术奠定基础的动力——一切从现在开始都是来得及的。
The point is: if you have even minimal data requirements and implement a predictive analytics software that includes explainable predictions, you’ll still benefit from understanding performance drivers and automation, even if the predictions aren’t 100% spot on.
关键是：如果你只有很小的数据需求，和只需要一个包含可解释预测的预测分析软件，你仍然可以从理解性能驱动程序和自动化中获益，即使预测不是100%准确。
We need to remember that our decisions are only as good as our data, and our data are only as good as the technology we use to understand and act on them. When executed according to the framework and principles I’ve laid out for you here, the journey to predictive itself will result in data-driven decision-making based on data-accurate, data-efficient, and data-masterful financial processes.
我们需要记住，我们的决策取决于我们的数据，而我们的数据也取决于我们用来理解和采取行动的技术。如果按照我在这里为你列出的框架和原则执行，预测本身将导致基于数据精确、数据高效和数据精通的财务流程前提。
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