Whenever we take any decision in our day-to-day life, it is by thinking about what happened last time or what will happen by choosing that decision. This is nothing but analyzing our past or predicting the future and making decisions based on it. For that, we gather memories of our past or dreams of our future. So that is nothing but data analysis. Now same thing analyst does for business purposes, is called Data Analysis.
每当我们在日常生活中做出任何决定时，都是在思考上次发生的事情或选择该决定会发生什么。 这无非是分析我们的过去或预测未来并据此做出决策。 为此，我们收集了对过去的回忆或对未来的梦想。 因此，这不过是数据分析。 现在，分析师出于业务目的所做的同样的事情称为数据分析。
You may ask this question: Why do we need to analyse data and why is data analytics so important? Well, to grow in life and do be better, we always analyse our previous decisions and try to do better next time. Similarly, Data Analysis is required to study the previous decisions made by a business, acknowledge the mistakes, and develop a model to be better in the future
您可能会问这个问题：为什么我们需要分析数据，为什么数据分析如此重要？ 好吧，为了生活发展和变得更好，我们将始终分析以前的决定，并在下次尝试做得更好。 同样，需要数据分析来研究企业先前做出的决定，承认错误并开发模型以使将来变得更好
Data Analytics and more importantly, Data Science is called the “New Big Thing” or “The Future of Technology” but the question really to be asked is this: Is Data Analytics relevant in a tech-driven future?
数据分析，更重要的是，数据科学被称为“新事物”或“技术的未来”，但真正要问的问题是：数据分析与技术驱动的未来相关吗？
The Present
现在
Before we answer that question, we first must see what real life applications Data Analytics is being used for in the present time. Currently, Data Analytics is used in many domains of the world, particularly:
在回答该问题之前，我们首先必须了解Data Analytics目前正在用于哪些实际应用中。 当前，数据分析已在世界上的许多领域中使用，尤其是：
· Marketing and Sales: Sales analytics and marketing tends to be more advanced and complicated, at least in B2C commerce. Data like customer segmentation and personalization, social signal mining, pricing and customer loyalty must be thoroughly analysed before developing a model for the e-commerce and sales to smoothly function.
· 营销和销售：至少在B2C商务中，销售分析和营销趋向于更加先进和复杂。 在开发使电子商务和销售平稳运行的模型之前，必须彻底分析诸如客户细分和个性化，社交信号挖掘，定价和客户忠诚度之类的数据。
· Operations: Advanced analytics in Operations tends to be on the lower maturity side. This is usually because opportunities are harder to spot and cross-business domain knowledge is required to create a step change. Data and analytics use in operations has traditionally included identification of new oil and gas drilling sites but has now come to include mining sensor data for predictive maintenance, integrated and demand-driven workforce management and real time scheduling optimization.
· 运营：运营中的高级分析往往处于较低的成熟度方面。 这通常是因为很难发现机会，并且需要跨业务领域的知识来进行逐步更改。 运营中的数据和分析使用传统上包括识别新的石油和天然气钻探地点，但现在已经包括了用于预测性维护，集成和需求驱动的劳动力管理以及实时调度优化的采矿传感器数据。
· Data Driven Ventures: A few firms have started to explore the merits of big data and advanced analytics, to expand their current business model, by developing analytical insights to offer as a service to its customers. Examples include credit card companies providing data-driven customer targeting, or telecom companies selling location data for traffic monitoring and fraud detection.
· 数据驱动型风险企业：一些公司已开始探索大数据和高级分析的优点，以通过开发分析见解为客户提供服务来扩展其当前的业务模型。 例如，提供数据驱动的客户定位的信用卡公司，或销售位置数据以进行流量监控和欺诈检测的电信公司。
So, we can clearly see that data analytics is being widely used in many domains, including technological as well as non-tech related domains. This shows us that big data and analyzing data is very important in this tech-driven world. But what is the future of Data Analytics? based on the information we seen above, we can predict the future of this technological trend.
因此，我们可以清楚地看到，数据分析已广泛应用于许多领域，包括技术领域以及与非技术相关的领域。 这向我们表明，在这个技术驱动的世界中，大数据和分析数据非常重要。 但是数据分析的未来是什么？ 根据我们在上面看到的信息，我们可以预测这种技术趋势的未来。
The Future
未来
Data analytics is constantly evolving. It started with descriptive analytics, which merely described data. Now, we are at a stage where analytics can predict future outcomes in the form of predictive analytics. Thanks to new technologies, like cloud computing, AI, IoT and machine learning, analytics is taking on new forms to complete even more complex operations. Some of these new technologies that can have a big impact on data analysis are:
数据分析在不断发展。 它从描述性分析开始，该描述性分析仅描述数据。 现在，我们处于分析可以以预测分析的形式预测未来结果的阶段。 得益于云计算，人工智能，物联网和机器学习等新技术，分析正在采用新形式来完成更复杂的操作。 这些对数据分析有重大影响的新技术包括：
· Augmented Analytics
· 增强分析
When machine learning and natural language processing is integrated into data analytics and business intelligence, it creates augmented analytics. This form of analytics is going to play a huge role in analyzing data in 2020. Augmented analytics is going to be the future of data analytics because it can scrub raw data for valuable parts for analysis, automating certain parts of the process and making the data preparation process easier.
当机器学习和自然语言处理被集成到数据分析和商业智能中时，它将创建增强的分析。 这种分析形式将在2020年的数据分析中发挥巨大作用。增强分析将成为数据分析的未来，因为它可以清理原始数据以用于有价值的部分进行分析，自动化流程的某些部分并制作数据准备过程比较容易。
· Relationship Analytics
· 关系分析
The ability to connect different data sources using several analytical techniques can transform data collection and analysis methods because it allows organisations to maximize the value of their data network and infrastructure. For example, relationship analytics allows organisations to optimize several functions at once, like account renewals, account servicing and pipelines. Salespeople will get a 360-degree view of their customers, allowing them to be smarter and targeted in their marketing campaigns. Relationship analytics is the future of data analytics because it gives organisations that extra dimension to their data analytics procedures.
使用多种分析技术连接不同数据源的能力可以改变数据收集和分析方法，因为它使组织可以最大程度地利用其数据网络和基础架构的价值。 例如，关系分析使组织可以一次优化多个功能，例如帐户续订，帐户服务和渠道。 销售人员将获得客户的360度视角，从而使他们变得更加聪明，并在营销活动中有针对性。 关系分析是数据分析的未来，因为它使组织在其数据分析过程中拥有更多的维度。
· Continuous Analytics
· 连续分析
In the past, data analytics platforms could deliver insights in a few days or weeks, and it would be completely acceptable. However, with the proliferation of IoT devices, the future of data analytics will expect platforms to generate even faster insights, to take full advantage of IoT devices. This is where Continuous Analytics comes into play, it allows organisations to continuously analyse streaming data, so analysts can shorten the window for data capture and analysis. The level of analysis may depend on the speed of delivery analytics teams are looking for.
过去，数据分析平台可以在几天或几周内提供洞察力，这是完全可以接受的。 但是，随着物联网设备的激增，数据分析的未来将期望平台能够产生更快的洞察力，从而充分利用物联网设备的优势。 这是Continuous Analytics发挥作用的地方，它使组织能够连续分析流数据，因此分析师可以缩短数据捕获和分析的时间。 分析的水平可能取决于分析团队正在寻找的交付速度。
· Augmented Data Preparation and Discovery
· 增强的数据准备和发现
The future of data analytics will see data discovery and preparation change, in a practice known as augmented data preparation and discovery. Traditional methods often involve rule-based approaches to transform data. However, augmented data preparation makes the process more flexible because it automatically adapts fresh data, especially outlier variables.
总而言之 ，数据分析对于技术的进步以及总体而言对新业务模型的未来至关重要。 机器学习，人工智能和物联网等技术趋势的各种进步将对大数据领域产生巨大影响，并将对取代执行数据分析的传统方法及其一般应用产生巨大影响。 它还将影响高度依赖数据分析来开发其营销和客户满意度模型的商业和销售部门。
https://www.mckinsey.com/business-functions/operations/our-insights/data-and-analytics-why-does-it-matter-and-where-is-the-impact
https://www.mckinsey.com/business-functions/operations/our-insights/data-and-analytics-why-does-it-matter-and-where-is-the-impact
https://seleritysas.com/blog/2019/12/06/preparing-for-the-future-of-data-analytics/
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