Implementing analytics for the long-term is not an easy feat. Holistic planning is required by leaders to guide the entire organization towards a clear direction. But how does the strategic process begin?
In a world where information is power, it is crucial that data is properly understood and best presented to decision-makers. Having a robust long-term analytics strategy in such cases is quintessential. However, few can do it right due to the deluge of data and the availability of so many complex tools. Furthermore, business trends are accelerating so quickly that any delayed decisions can end up with lost opportunities.
In this article, we focus on how companies can minimize headaches and effectively develop a long-term analytics strategy.
tip 1: goals and objectives
A good strategy starts with the goals and objectives your business before even considering what data is available within the organization. It is only when the first two are established that data can be used to achieve those milestones.
tip 2: Defining Business Challenges
Cutting-edge companies often develop their data strategy in a clever way to avoid drowning in irrelevant data. Each of its data points address a specific business need, generate value and help it to achieve its goals. This means first defining your most pressing business challenges and developing useful questions related to these operations; only then can you begin collecting and analyzing the correct data to answer those questions.
Tip 3: Data Strategy – A leader's domain
A robust strategy should not be an additional burden to the IT support team. In fact, this can sometimes be detrimental to a business. There is a widespread misconception among business executives that data and analytics is a matter for the IT department. This demonstrates a lack of understanding of how data works. Data strategies which are driven by the IT team have a tendency to focus on data storage, ownership, and reliability, rather than the business’ long-term strategic goals and the valuable role that analytics can play in achieving those goals. It is for these reasons the data strategy should be maintained by the leadership team.
Tip 4: Importance of a data-driven CULTURE
Identifying the critical data points and using it strategically varies from one company to another. With so much data available, leaders need to develop a data-driven culture so that everyone can focus on finding the exact, specific pieces of data that is most valuable. To find out how a data-driven culture can be developed, read our blog article here.
Step 1: It starts with a vision
The way an organisation collects, manages and stores data can affect its ability to identify risks and opportunities. Data-driven culture implies that all business decisions across departments are guided by data that is align with business objectives. Data stored within your business can help initiate the development of your organization and it should be the key driver of your business decisions.
Step 2: Manpower
Evaluate if hiring an in-house data science team or outsourcing analytics to third party companies achieves organisational goals better. Read our blog article here for guidance on your evaluation process.
STEP 3: Organize your data
The data is there. You need to identify it, clean it and make it usable. Firstly, data must be organized. This can be done in groups. Once the data has been grouped, it can then be systematically categorized and coded, and the differences between one category and the next is distinct. That analysed data needs to be organised, visualized, and made accessible so that it can be interpreted and used for business decision-making purposes.
Step 4: Quantify it
Quantifying data gives management and staff the motivation to take things forward. It ensures that internal data maintains a high level of quality so that decisions based on the information is accurate.
So what does quantifying data mean? It is an important business component where the organizations decides – will it reduce costs, generate revenue, reduce risks, create opportunities, or all of the above?
However, this process isn’t simple. For this to succeed, businesses must first establish data governance, data quality and data architecture, and just as importantly, appoint somebody to lead this process. This is followed by the creation of a comprehensive inventory of all business data. Once completed, the critical step of data categorization can commence, based on the above-mentioned data governance principles. This is a very high level summary of data quantification, and you should ideally consult experts if this is new to you.
Step 5: Encourage Teamwork
Oftentimes, the function(s) of data science emerges organically in larger organizations. Individual managers realize the need to excavate the valuable information they have on hand. While this is an enlightened approach (especially when compared to no data analytics function), it presents the risk of creating silos within business units, so that no one communicates with one another. Business managers need to encourage interdepartmental teamwork and communication, with the ultimate goal of maximizing the potential of available data.
Step 6: Data Management
A data strategy must include plans for the storage and management of the data. This decision process must include multiple business units to ensure the data is accessible and safe.
For companies with less resources, they can consider identifying key data points that drive their business metrics and focus on first storing them. As the company evolves in its data analytics capabilities, they can begin to consider creating data lakes.
For larger enterprises, many often store its data in ‘Data Lakes’. They are repositories of raw data in any form (structured and unstructured). Stored data can be in any format, including text, audio, video, and images. However, good data management is essential to prevent a data lake from turning into a data swamp. Data lakes are less structured and more flexible, with no processing required until data retrieval. Additionally, data lakes are typically inexpensive by design.
Step 7: Contingency Planning
As ever, contingency plans state the responses necessary for any less-than-ideal situation that might impact the business. These plans help to define:
- information systems and components that support key business functions
- system recovery strategies in case of disruptions
- procedures for restoring critically damaged systems
- system testing, system maintenance and personnel training procedures
Click here for more insights into contingency planning.
The supply chain is one of a provider’s largest cost centres and is one of the most significant opportunities for healthcare organizations to reduce unnecessary expenditure and improve productivity.
Predictive tools are in high demand by hospital executives looking to reduce variations in supply management, and to gain more actionable insights from the data they have on past ordering patterns and supply utilization.
Using analytics tools to monitor the supply chain and make proactive, data-driven decisions about spending could save hospitals almost $10 million per year. Both descriptive and predictive analytics can support decisions to negotiate pricing, reduce the variation in supplies, and optimize the ordering process.
Data analytics strategies can start small as a litmus test to lay the foundation for broader transformation projects. When long-term analytics plans are robust, they generate returns to help fund later stages of the strategic implementation. Throughout the process, companies must draw on knowledge from their early wins to create a road-map for company-wide transformation, “industrialize” its data analytics, and build systems and capabilities to execute new data-driven strategies and processes.
Companies such as DataVLT have been working hard at democratizing data analytics for businesses of all sizes. DataVLT exists to help businesses to unlock the power of their data and to take their data analytics to the next level. Find out how your business can gain invaluable decision-making insights here.
DataVLT is an affordable, on-demand analytics platform secured by blockchain technology. It is designed to simplify the complexity of data science. Backed by artificial intelligence and machine learning capabilities, DataVLT empowers enterprises to make meaningful sense of their big data and scale cost efficiently. Essentially, it is an end-to-end data/information management platform.
Learn more at www.datavlt.com