March 24, 2021
Today, as the world becomes smarter and smaller, data has emerged as the key to competitive advantage. A company's ability to compete depends on how well it can leverage data, apply analytics, and implement new technologies. According to the International Institute of Analytics, by 2020, businesses using data will see $430 billion in productivity benefits over firms who aren't using data. It is no secret that data is a key business asset, and is revolutionising the way companies operate, across sectors and industries. Every business, no matter its size, needs a robust data analytics strategy.
Having a data-driven business strategy is important when you consider the sheer volume of data circulating these days. Businesses tend to get caught up in the Big Data buzz and start to collect data without considering what to do with it all. They get overwhelmed with options. Which is why a strategy is required. A good data strategy isn't about what data is readily available. It is about your business's goals and the role of data in achieving those goals.
Companies haven't shied away from long-term investments in data management. However, the problems continue to grow. One reason is that companies perceive data as a part of a technology project, instead of treating it as a corporate asset. Hence, companies felt that traditional application and database planning efforts were enough to address ongoing data issues. Corporate data requires an effective data strategy.
The idea behind creating a data strategy is to ensure that all data resources are positioned in an effective way, to be shared, used and moved easily and efficiently. No longer a by-product of business processing, data is today a critical asset that enables processing and decision making. A good data strategy helps by ensuring that data is managed and used just like an asset. A data strategy establishes common methods, practices, and processes, to manipulate, manage and share data across the company in a repeatable manner. Some companies have multiple data management strategies running at the same time like metadata, master data management, data governance, data migration, modernisation, data quality, data integration, et. Hence, most efforts are focused on point solutions that solve project or organisational needs. A data analytics strategy road map aligns all activities across data management discipline, in a way that they complement and build on each other, to deliver better results.
How to build a data strategy?
To create a robust data and analytics strategy, business leaders need to take a number of factors into account.
In order to find the right data, you need to define how you want to use the data. Certain types of data fulfil some goals, while other variations fulfil other goals. The first step in how to build a data strategy is to know what you really want to use that data for.
How to gather the data.
Once you identify what you want to achieve with the data, you can move to the sourcing stage. You need to collect the best data to meet your needs. There are many ways to source and collect data, including accessing or purchasing external data, putting in place new collection mechanisms, or using internal data.
Turn data into insights.
A solid data strategy needs to plan how you will use analytics on your data, to extract any valuable insights that can help you with decision making, operations improvement and value generation in your business.
Once you have decided how to use the data, what kind of data you need and how you want to analyse said data, the next step in creating a robust data strategy is to consider the technological and infrastructural implications of those decisions. You need to decide on the hardware or software that will turn your data into insights.
Internal data competencies.
You need to cultivate certain skills, to get the most out of your data. You can develop data-related competencies in your organisation using two methods. One is by boosting in-house talent, and the other is by outsourcing data analysis.
Collection and storage of data, especially personal data, brings with it major legal and regulatory obligations. It is important that companies consider data ownership, privacy and security issues, when drawing up their data strategy. Failure to address these issues could see data turn into a huge liability, from an asset.
Benefits of a data-driven business strategy.
Push sales and customer loyalty
When you collect data, you get a lot of customer information about the latter's preferences, beliefs, etc. You can then use a tool like SplashBL to tailor products and services, by decoding the data and finding out what the customer wants. If the customers have a good experience, your sales will increase, and your clients will become more loyal.
One of the advantages of data-driven strategic planning is that firms can predict the future, and reduce the potential financial impact. You can find patterns in product and service pricing, and take appropriate steps to reduce costs.
Your confidence with regards to investing in developing new offerings will increase, thanks to social listening. Thanks to the ability to finding out what your target audience is interested in, you can figure out whether they want something new, or a revision of the old product offering. This unique customer insight builds confidence.
Increase productivity and efficiency.
If you use Business Analytics Software, you can identify revenue-generating opportunities, boost operational efficiencies, and increase and optimise production. Businesses may also use a Business Analytics tool to forecast inventory needs, in order to increase the efficiency of their production.
Process more data.
Thanks to the rise of AI and IOT, businesses have begun to amass more data, thus making the processing of data tough. However, companies can process data if they use software with AI and machine learning. Processing more data means getting valuable business insights.
A Data Analytics tool helps firms reduce potential risks, by giving firms vital insights about the success of new products, considering their customer base. The tool can allow businesses to predict early, and reduce the risk of downtime and asset failure.