The Importance of Being a Data-Driven Company
With its well-developed digital infrastructure, it is no wonder that Singapore topped the Asian Digital Transformation Index and expects almost all governmental services to go digital by 2023. Singapore SMEs who embrace digital transformation also expect to see average revenue gains of 26%, and have an appetite to adopt Big Data analytics, Artificial Intelligence (AI) and Machine Learning solutions. This is reflective of the global business arena, which is increasingly hyper-competitive and fast-paced. The need to keep up to speed and to stay ahead of the curve translates to a pressing need for data insights. Aligned with the industry-wide buzz about big data, this article will discuss about the importance of setting a goal, building a data collection strategy, collecting and analysing data, protecting data, as well as evaluating insights.
The first step to drawing insights from data is to first know what kind of insights you need or want. After pegging a time frame to the goals, segment them into short-term and long-term ones. Ensure that they are tied closely to the organisation’s overarching mission and values before listing the information that you need to collect from stakeholders to realise the goals.
Setting S.M.A.R.T goals is key. Goals that are specific, measurable, agreed upon, realistic and time-based will go a long way in optimising the insights of the data gathered.
Brands looking to break into a new market with a product line expansion, particularly in a new geographical region, will require information about the existing and projected market conditions. Shareholding of various competitors, preferences and price sensitivity of target consumers, possible collaborations, and legal requirements, just to name a few. Some goals to look into include awareness building, consumer retention, product development, consumer demand analysis, and more. It is good to brainstorm and shortlist a couple of goals before deciding on the perfect one to execute.
Come up with a data collection strategy
Poor data practices cost the US economy about US$3 trillion a year. As such, knowing the pitfalls to watch out for is extremely important. Architecting an enterprise data strategy is not simple, but is by no means impossible. Having a flexible and open platform is a good start. In line with this, opting for an open source-based platform can prevent the company from being locked in with a single provider which can get very costly considering the expensive proprietary software and systems. Pacing the team alongside both the ever-evolving technology and a clear data strategy is important to enable this.
On a smaller scale, companies can start by doing up a flow chart to see the channels whereby data can be collected. Teams can get together to communicate exactly what has to be measured, and how everyone can go about doing it. Depending on the company size and the allocation of resources, there may or may not be a need to have a data management team to help spearhead the consolidation and checking of information.
Start collecting and cleaning data
Going paperless and having all information digitised is an essential step to grease the wheels of data collection. Gathering good quality data is critical to establish great results. Communicating a preset template and guiding instructions for all to follow helps. Scheduling the reporting of data allows the collection process to be smooth and timely. The data management team should also ‘clean’ the data and confirm that everything is in working order. For instance, standardising the unit of measurement and ensuring that as little factors are left blank as possible are just elementary things that one can do to safeguard data quality.
While the data management team can spearhead this initiative, cross-department collaboration is paramount to facilitate the effectiveness of data collection and to reduce unnecessary work duplication as much as possible.
Protect all data
In today’s digital world, conventional trade has made way for virtual dealings. Governing the flow and exchange of data is hence increasingly imperative, and is something that the law will eventually keep up with. Data localisation, for one, is more and more prioritised, especially in countries that restrict international data transfers like China. In such cases, companies may need to factor in local data storage and processing costs. Over time, a good data system can eliminate data silos, and instead, integrate and incorporate information into useful data lakes. Data protectionism is definitely something to bear in mind when dealing with data collection and processing. Keeping data secure and reliable can greatly aid a company in its journey towards digital transformation.
Often times, companies struggle a lot with the analysis of data. Crunching numbers can lose its meaning if the goals set earlier were not well thought out. Here are some analytical techniques available to consider.
- Benchmarking compares the current level of firm performance with previous ones, or that of close competitors’.
- Retrospective Analysis examines how historical information can account for a specific outcome.
- Share of Voice looks at a brand’s media coverage as compared to close competitors.
- Standard Key Performance Index (KPIs) determines of a business goal has been met.
- Predictive Analysis employs existing data such as statistics to predict future outcomes.
At the back of your mind, remember that qualitative and quantitative analysis should always go hand in hand.
List your insights
After the analysis, collate the information and link it to the goals set earlier. Look at the insights and act on them. You may tweak an existing marketing campaign, plan a new launch for your product, and even schedule communication efforts better with the insights. You can also present them to the management to determine resource allocation and regional expansion plans.
It is crucial to allow convenient access of such insights to relevant stakeholders through a multitude of simple and self-service applications. Data ware projects can fail easily when users cannot get their hands on the data when needed.
Being a data-driven company is important. Companies may have to consider handling data in-house to cut costs, eliminate barriers associated with agencies, and generate better quality insights in the long run.