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How SMEs can become more Data-Intelligent in 60 Days

The Evolution of Data Analytics and the impact on Valuation

Big Data and data analytics as a theme have become mainstream in the recent years, although the use of data for business decision-making is hardly new. In the late 19th century, Henry Ford had already used operational data to decide on how best to manufacture Ford automobiles – he measured the time required for each process on the assembly line.

Analytics for Business however became more commercial in the late 1960s when computers started to play a bigger role in decision support systems. Since then the confluence of cheaper and more powerful computers, better ERP software, more efficient data warehouses and the explosion of the internet have created an unstoppable virtuous circle in analytics development.

Today, Big Data is a buzz-word and not a day passes without us encountering this term in the media. To the uninitiated, Big Data refers to data-sets which are so complex and large that traditional methods of data-management are inadequate. New technologies such as Natural Language Processing and Machine Learning, Cloud Computing, Internet-of-Things, and Visualisation software have emerged to help organisations make sense of this data.

It then appears that data is the key to the survival and sustainability of any organisation in the future. I make this assumption because in today’s world, your competitors will be using data to make better decisions in marketing, finance, product development, infrastructure and distribution. The organisation which then ignores the data revolution will face the peril of extinction in the next five to ten years.

How to improve your valuation techniques using data analytics

Data Analytics and SMEs

The Singapore Government announced in Feb 17 the recommendations put up by the Committee of Future Economy. A key thrust is the Building of Strong Digital Capabilities where data analytics and its usage become a regional or even global competitive advantage for big and small companies.

Many SMEs however have not harnessed the potential of data analytics for a variety of reasons. The most common reasons are the lack of a practical framework, the failure to appreciate the use of data analytics for business-building, a company culture resistant to change, and the lack of knowledge on government support.

In this article, we will discuss practical ways which can help SMEs to become more data-intelligent in 60 days.

We present the 4Rs to making your SME more competitive with data analytics in 60 days.

FIRST "R" - Right Framework

A framework is a high-level guideline to ensure all key components of any Business initiative are covered. A framework gives you a direction to proceed, and helps you to identify the gaps in your plan.

We will touch briefly on the key elements of a data analytics framework.

The Data-Analytics Framework for SMEs

Setting up your company's data analytics framework

1. Management Assessment (~1 Week)

The first step is for Management to assess quickly whether they are already using data to make key decisions. It is very common for “Tow-Kays” to make decisions based on gut-feel. However, as the business environment becomes more complex, it is worthwhile for Management to systematically think about what information they need to run their business better.

Ask yourself this question, “What are my Business Objectives, and do I have the information now to support better decision making”

For example, in determine a product selling price, it is important to understand the full cost of manufacturing the product.For a manufacturer, this would mean obtaining historical data on raw materials, overheads and manpower. This is even more critical if the factory produces many products. With such information, the manufacturer can price for profitability or optimise production cost.

In another example, say the goal is to improve customer retention.One of the metrics can be percent of customers renewing their subscriptions, and the business levers can be the design of the renewal page, timing and content of reminder emails, and special promotions.

2. Data Management

Data Acquisition (~3 Weeks)

Firstly, think about how you are going to obtain the data which you require to meet your Business objectives. In SMEs, data is often not as widely available and not managed properly.

Have conversations with people in Finance, Operations or Sales to get a feel about how much data they have and whether they have what you require. These conversations can be awkward because you may realise the entire organisation is running on gut-feel.

Getting the data you require is an iterative process, and only focus on the most important KPIs which you need for now. If your company does not have an ERP system, then you may have no choice but to devise manual data-collection methods for now.

Conversations to obtain KPIs is your “gap-analysis” to understand what important data is not available in the Organisation.

Data Quality and Management (~2 Weeks)

With all the KPIs collected for a reasonable period, you will clean the data. This is the most critical step in the data value chain—even with the best analysis, junk data will generate wrong results and mislead the business. Common data errors include spelling mistakes, missing information or nonsense information.

To clean up the data and manage the data sets, for a start, you will need probably a Finance person to manage the data, and the data-owner (e.g. operations, marketing, sales) to clean up and validate the data.

It will likely be that common spreadsheet programs such as Excel will be used to manage the data-sets first. It is important then to ensure version control so that we do not work with outdated data, and have access control so that only authorised persons can use the data.

Data Governance (~2 Weeks)

Data governance is about ensuring your Company has the procedures, policies and technologies to manage the availability, usability, integrity and security of your data.

For a start, work with your IT department to ensure data security, and HR for example to set up access rights privileges.

3. Data Analysis (~2 Weeks)

This is the most value-added step where we will start to turn clean data into insights. Larger organisations have dedicated data-science teams to build data models.

However, it is just as important to have someone who understands the business well enough to recognise whether the results of the models are relevant and applicable.

For SMEs, data analysis can be done by a Business analyst, or a Finance person together with the Department owner of the data.

Always be asking the question,” What is the business message from this piece of analysis”

It is also important that senior Management discusses the validity of the analysis and findings.The combination of business experience and hard facts is a powerful generator of usable insights.

4. Action Plan based on Data Analysis (~2 Weeks)

At this stage, the Company will have data and insights to support Management in decision-making.

For example, if Management’s objective was to reduce the production lead time so that goods are shipped more quickly to customers. Then the Company would probably have gathered operational data such as production line down-time, rejection rates and the reasons, supplier delays and frequency and reasons why, etc.

The action plan would then be for Production to work closely with the Engineering teams on reducing production down-time, liaise with Purchasing to improve suppliers’ performance, and trouble-shoot quality issues with Quality Control. There will be KPIs which the Company can subsequently measure.

At this Stage, it is important that action plans are consultative and collaborative, Company-wide. Negatively performing KPIs should not be used to blame, but rather used to improve Operations.

SECOND "R" - Right Culture and Mindset

"If you want something new, you have to stop doing something old”

Peter Drucker (1909 - 2005)

Peter Drucker was considered the “Founder of modern management” and he had been a strong advocate of change because he had known that nothing stood still. You either move ahead, or fall behind.

The biggest challenge facing SMEs is the speed of change. Many SMEs in Singapore can be family-run businesses and the ways of business have been unchanged for many years. In addition, the pressures of day-to-day operations have left many SMEs in “fire-fighting” mode permanently. Often, there is very little budget to innovate.

However, the good news on using more data analytics in the organisation is:It does not have to be expensive and you do not need PhDs. to get started.

Having the courage to change, and the right mindset to accept new ideas is Free-of-Charge!

There are a few practical ways to create the right culture and mindset. This includes having the owners support the idea that the Company needs to use more data to improve the Business. Buy-in at the top-level often makes company-wide adoption smoother and quicker. However, if the owner is resistant to change, then it may be a good idea to start small in a low-cost and low-risk manner, and show the owner the results.

There should also be lots of coaching for people who may be resistant to change, or may feel threatened by this disruption. In fact, this presents the perfect opportunity to upgrade the capabilities of key employees.

What culture do we want to move to? We want to move to a more management culture.

THIRD "R" - Right People

We had earlier said that in the first 60 days, we could probably use existing resources to jump-start the data-analytics journey. However, as the Organisation grows, it is necessary to hire the right people to professionally manage and spread data-analytics throughout the Company.

This could be permanent staff, or external consultants and solutions specialists.In reality, there are data analytics specialists for different specialisations e.g. marketing, operations, customer engagement.

FOURTH "R" - Right Support from Government

SMEs are incredibly fortunate to have significant government support on data analytics. Spring Singapore has for example the Capability Development Grant which SMEs can tap on. There is the popular Productivity and Innovation Credit (“PIC”) grant from the Inland Revenue of Singapore. Infocomm Media Development Authority has the iSprint initiatives to help SMEs adopt more technology including in data analytics.

It is important the SMEs understand fully the support that is available to help them on their data analytics journey.

The Journey Ahead

It is a well-known fact that SMEs form the backbone of the Singapore economy. It is therefore important the SME sector is vibrant, innovative and sustainable. Having the right mindset to embrace data analytics in Business decision-making will be a key competitive advantage for SMEs.

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