Agile transformation,

From “interconnection propelled by application” to ” application propelled by interconnection”

The Big Data industry has been developing for ten years, and so has my bonding with Big Data. As a new factor of production in the digital economy, data is closely related to its ability to drive business; that’s what we call “data power”.

As one of the pioneers in the big data industry, I have been committed to promoting the popularisation and application of data thinking. From my book “The Business Revolution of Big Data” to “The Nature of Big Data”, I hope readers will be able to understand the following truth: Big Data is not far away, and it is rapidly and profoundly changing our society, environment, economy, and even our lives.

Over the last few years, I’ve been serving as a consultant to government agencies and many corporates worldwide to help them implement Big Data projects. With a data thinking mindset, I have exalted my experience into a practical methodology. I want to share my experience humbly and, at the same time, draw inspiration from those with common interests.

I hope everyone living in this era will have a more practical understanding of implementing big data and succeed in digital transformation for our cities and corporates to build a better and sustainable world.

【Ten years of data thinking and reflection】

Data, a new frontier for every country and enterprises

The premise of using big data well is to “assume that data is available and not be limited by the amount”, which I have always taken to heart. In the age of Big Data, we need a new way of thinking, and it is far more important than data resources and any algorithms.

I think we are still a long way from the day when artificial intelligence will rule the world. But the winners will always be and should be those who have faith in data because they believe that the purpose of technology is to make people’s lives better and happier.

Looking back over the past 20 years, I feel fortunate to have worked for data-driven pioneers such as Microsoft and eBay. I am even more excited to have been part of a world-renowned data company such as Alibaba.  My dream of Big Data is realised along the journey.

After completing my mission, I left Alibaba in 2016 to join Sequoia Capital as a venture partner, with the initial intention of gaining a broad and deep understanding of the difficulties and opportunities encountered by different players in the digital ecosystem.

As a pleasant surprise, this period coincided with the accelerated promotion of a data-driven digital economy in various cities such as Beijing, Shanghai and the Guangdong-Hong Kong-Macao Greater Bay Area. Therefore, while working as a data strategy and governance consultant for some internet companies with large amounts of data, I was also commissioned by the Chinese Association of Hong Kong and Macao Studies under the Hong Kong and Macao Affairs Office of the State Council to complete the Research Report on “Data-driven Guangdong-Hong Kong-Macao Greater Bay Area Innovative Development Planning Research Report”. In addition, as a consultant to the Beijing Municipal Government’s Big Data Promotion Group, I also wrote the “Big Data and Precision Urban  Governance Report”.

What I’ve seen and heard over the past ten years has gradually made me determined to propose a top-level design for digital transformation, from strategic thinking to actual implementation. From ” The Business Revolution of Big Data ” in 2014 to “The Nature of Big Data” in 2017, I have always believed that data is an essential resource for the future evolution of business and society and that a top-level design, a practical approach that combines technology and business for the new era is urgently needed on the road to change.

I hope to use the previous two books to strengthen the closed-loop thinking of “interconnection propelled by applications, which bring further applications” in government agencies and enterprises to speed up their digital transformation while helping to change the role of organisational structure in technological innovation.

The logic of a data-driven mindset constantly impacts the inherent way of learning, organisational behavior, and business operations’ underlying logic. Some corporates have compared this change to “changing the engine of a flying plane”.  This analogy is not an overstatement, and the impact of this change may go far beyond what we imagine. If the organisational structure of the enterprise does not complete the transition successfully, the whole organisation may be out of control.

With unmanned driving, robots and artificial intelligence, technology has become a means of innovation for many corporates. However, whoever has mastered the various super applications that people use in their daily lives, such as digital maps, eCommerce, search engines, personalised recommendation applications, ride-hailing applications, mobile payment applications, government service applications, credit rating-related applications, etc., will accumulate a large amount of data resources and invariably influence and control various scenarios of people’s lives in the society.

Recently, there has been a lot of unexplained turmoil that is heralding a new era.  We are at a tipping point between this old and the new era, and no one can tell us what the future will look like, but having a data-driven capability is undoubtedly one of the major influencing factors. I predicted in my book “The business Revolution of Big Data” in the year 2013 that data will become a battlefield for countries and corporates in the future. The importance and urgency of this are already self-evident, judging from the cross-border data regulations countries have roll-out from 2016 to 2021.

[Ten years of data experience]

Five significant stages, the critical prerequisite for digital transformation

Consolidating my Six years of experience in Alibaba and my consulting experience in JD Digital in recent years, I want to explain the connotation of data-drivenness into five stages:

Stage 1: Using data as the aiming device for decision-making

We first hope to use data as an aiming device to help companies understand the status quo and make favourable decisions for their business development. These decisions include customer acquisition, product development, marketing planning, risk management, and resources allocation. However, as I mentioned in the book “The Nature of Big Data”, it’s not easy to form a closed loop between data and decision-making from the perspective of business analysis. Therefore, the lack of data (e.g. High cost, Technology barrier) will make it impossible to complete the “decision, action, and result” process.

More importantly: Is the data for performance indices sufficient for helping us to identify problems, figure out the reasons, and arrive at solutions?

At this stage, we use data analysis to assist human intuition (experience-based assumptions) in making decisions. Then optimise by using data to measure whether the previous assumptions are valid. In this way, we have a vague closed-loop decision, but the problem is that the more ambiguous the answer, the longer the iteration process will take.

To gain experience and make the less connected closed-loop decision traceable, it is essential to be proactive in adding data that has not been collected or utilised to strengthen the metrics, data tracking and visualisation systems. Managers need to lead as role model and make decision-makers at different levels more aware of and capable of making data-based decisions.

In JD Digital’s monthly management meetings, the CEO often asks business executives about the details of data metrics and what they mean. It is only in this pragmatic environment that analysts, business people and product managers can play a “point and shoot” role.

A data-driven monthly management meeting should be like drafting military tactics over the  sandbox exercise in war.  I would advise you not to be obsessed with fancy big data Screen. A good looking ‘dashboard’ doesn’t always work well.

Stage 2: Embedding data analysis into workflows

I believe that data-drivenness will only work when data capabilities can be generalised to the ‘nerve endings’ of the business, as simply equipping employees with good data analytics tools and awareness is like scratching the surface; you need to find ways to apply analytics tools to the decision-making process in the workflow.

A typical example would be during a festive sale when selecting products or suppliers for the product promotion page; category managers can use analytics tools that fit the scenario in the relevant systems and workflows. Improving the ease and value of analysis is necessary to generalise data capabilities, which is a process of popularising data-based analysis and a milestone in the digitalisation of the industry.

Another typical case is in the product deployment, where the data that needs to be collected must be thought out and equipped with the relevant data analysis functions when designing the product; otherwise, it is hard to obtain the data to uncover the business trends.

Stage 3: Data governance allows internal and external resources to work seamlessly together

Ali Finance was Alibaba’s first data innovation business, providing credit services to Small and Medium Enterprises (SME). Its data sources were initially mainly generated from transaction data on Taobao and Tmall, which could be used to assess risks and repayment cycles, etc. As a new business unit within the Group at the time, aggregating data from multiple sources across companies and maintaining sound data quality on top of that was the biggest test for Ali Finance.

Data governance generally involves the collaboration of internal and external resources; for example, minimising noise at data collection points and adding stability elements to data quality are parts of the data sharing process that cannot be circumvented, and the greater the span, the greater the difficulty. That is why big data integration in smart cities project is easily protracted, but the value it brings is very high once interoperability is achieved.

In the data governance consultancy work I’ve been involved in, the success of this stage directly impacts the probability of success in Stage 4.

Stage 4: Finding the right time to eliminate data silos

Every corporate has its own core business, and data resources, in particular, are more likely to find data-driven systems, such as Taobao’s product recommendation system, a bank’s risk control system, or a city’s Intelligent Operations Centre (IOC). Almost all larger internet companies have the problem of algorithms, analytics and AI teams working separately in different lines of business, and also because these teams generally each have their data platforms; thus, data silos are common.  Many corporates are only beginning to bridge the gap between data silos and integrate data because of the establishment of the Data Management Committee.

It was only through the determination of Ali’s “Temujin” (Lu Zhaoxi, Alibaba’s first CEO) to transform the company digitally that I was able to set up Alibaba’s Data Management Committee. Perhaps because of the success of Alibaba’s digital transformation, “how to build an effective data management committee” has become the question I am asked most often. The answer to this question varies depending on the maturity of the organisation in terms of digitalisation.

In many businesses, it’s a matter of timing to eliminate data silos. For example, if Meituan and Dianping had not integrated their data governance when they merged, it would have been much more challenging to deal with later. Having said that, do data silos have to be eliminated entirely? We will continue to discuss this issue in later chapters.

Stage 5: Core of Operation shifts to be data-driven  (Smartisation)

At an Alibaba president’s meeting, Jack Ma once asked: Taobao and Tmall customers are patronised 24 hours a day, so why don’t executives have to work overnight and on weekends? With this in mind, it led to Juhusuan Group Buying’s automation and the emergence of unmanned supermarkets and intelligent customer service.

As the first person to lead a team to develop this automation project, the strength of the data governance and the accuracy of the algorithms I experienced during this process were several notches above the previous four stages. Crucially, you find that data sources you hadn’t even considered collecting before and that many of the decisions you take for granted are uninformed.

The most interesting part of the automation process is that the business, technical, product and data departments all think they are the core departments for operations automation, so the first thing to fix before driving an automation project is the hearts and minds.

These five stages do not necessarily occur sequentially in a company’s digital transformation process, and the nature of the business varies from company to company. However, the most common mistake is to overlook long-term strategic needs in favour of short-term efficiencies.

Data governance is a long-term capability-building process easily overlooked in many corporates I have met in the digital era. I would therefore suggest that we focus on the following three key questions:

● Question 1: Is there someone in management dedicated to building a data strategy?

● Question 2: Has the scope of data governance been aligned and planned in line with the data strategy and prioritised for the long, medium and short term?

● Question 3: How can technology help reduce manpower in the process so that human resource is no longer a bottleneck in data governance, including deploying automation to improve the data productivity and efficiency of data use? It is important to note here that different stages of the strategy should use the appropriate governance approach and technology.

I have highlighted the difference between these stages because I have found that many businesses tend to focus on short-term solutions and ignore the systemic problems in data governance. Once they reach a stage where data becomes unwieldy, the business needs to grow rapidly, everything becomes purely demand-driven, and the underlying platform set up inevitably lags. Over time, these tasks, which require a great deal of internal coordination, become increasingly difficult until the resources within the organisation are depleted, causing the organisation to lose confidence in its long-term growth. There are three ways in which companies can improve this problem.

● First, try to take an initial inventory of data-related development projects in your organisation over the past 12 to 24 months. If you find that more than 70% of projects have been prioritised for short-term gain, be wary as this is likely to indicate a lack of focus on long term data governance within the organisation.

● Secondly, look at how data is being shared across the organisation. If you find that the same data is being stored repeatedly many times, this means that data usage chaos has festered.

● Finally, many corporates are at odds with data governance and business priorities. In large corporations such as BATJ, some even have more than 3 data platforms. It’s not uncommon to see this phenomenon, but how to deal with this conflict?

Suppose the above description sounds familiar to you and you are suffering from resistance to doing business properly as a result. In that case, my advice to you is that better data governance is urgent.

BATJ refers to the four major corporations currently at the forefront of digitalisation: Baidu (B), Alibaba (A),Tencent (T) and JingDong (J). –Editor’s note