Data is a virtual gold mine for enterprises, considering the profitable insights it can generate in today’s digital age. But a vast majority of enterprises underutilise their data. They sit on large volumes of data without being able to turn their data into actionable insights. Such dark data comprises about half of all data collected by enterprises. 

Data becomes a goldmine when leveraged to extract valuable insights and drive informed decisions. Here are steps to make the most of available data:

Build a data-driven culture

The basic prerequisite to benefit from data is to develop a data-driven culture across the enterprise. 

Encourage a culture that values data and uses data-driven insights when making decisions. The returns from such a data-driven culture are often intangible but huge. 

Also, train employees on data literacy. Encourage the rank and file to keep abreast of emerging technologies in the data and analytics space. 

Another key aspect of a data-driven culture is to promote a mindset of continuous improvement through data insights. The biggest data mistake is to rest on one’s laurels. Data strategy has to be an ongoing process. Review the approach regularly. Refine the approach based on feedback and changing business needs. Experiment with different data sources and analysis techniques to uncover new opportunities.

Define clear objectives

Getting usable and actionable insights from data depends on identifying the purpose upfront. The most common enterprise objectives include cost reduction, process efficiency, tracking leads, furthering personalisation, identifying fast-selling products, fixing internal issues, reducing waste, and improving customer satisfaction. Tracking data also unearths gaps in fulfilling customer needs or demands and driving innovation.

Begin with the end in mind. Identify the key questions for which the data has to provide answers. Otherwise, the data analytics will resemble a solution searching for a problem. 

Identify the data sources

Many enterprises collect data indiscriminately. Having too much data can be as detrimental as not having enough data. Collecting too much data leads to data overload. The enterprise raises expenses to store the data when such data is of little use.

Successful use of big data depends on collecting only the data that aligns with the objectives. 

Identify the sources of data important for the business. Business-oriented Big Data sources include

• Transactional data from point-of-sales systems.
• Machine data such as server logs, network logs, and RFID logs, 
• Data from websites, such as click-stream data or data of visitor interactions.
• Unstructured data from external sources such as emails and survey reports.
• Social media feeds, including online mentions about the company or brand.
• Cloud data such as stock ticker prices.

Gather data from the right, authentic sources. Also, make sure the data is current. Have a system in place to replace outdated data with the latest data. 

Integrate the data

Enterprise data lies scattered across databases and repositories. For instance, customer data, financial data, and operational data are separate, and some of such data may be in silos. 

Integrate data from these sources to create a comprehensive view and understanding of the business. To integrate data from various sources, connect databases using APIs, or create data lakes. Make sure the data storage is secure, accessible, and scalable.

Prepare a data analysis roadmap that outlines the data integration and segmentation methods, especially make plans for unstructured data. 

Give unstructured data the importance it deserves. All enterprises have a mix of structured and unstructured data. Structured data is the daily transactional information collected from operations, sales, and inventory. These data, stored in databases with a structure or schema, are easy to analyse. The challenge comes when co-opting unstructured data, such as social media content, that does not have a schema to store it. Analysing unstructured data has always been a big challenge for enterprises. Most enterprises skip it altogether. But today, most enterprise data comes unstructured, and enterprises can ill-afford to ignore it.

Ensure data quality

The raw data collected for analytics is often dirty. Dirty data means incomplete, inaccurate, or inconsistent data. Accurate analysis and meaningful insights depend on quality data. 

Cleanse the collected data to eliminate errors and inconsistencies. Cleansing involves tasks such as standardising fields, completing missing values, and deleting duplicates. The cleaner the data, the better the analytical insights. Clean data makes it easy to perform tasks such as segmenting prospects and driving targeted marketing campaigns. 

Establish data governance policies to ensure data security, privacy, and compliance. Make sure the data governance policies can handle the risks posed by artificial intelligence. This is crucial to maintain data integrity and build trust in the data.

Invest in top analytics and visualisation tools

There is no shortcut to investing in state-of-the-art analytics and visualisation tools.

The type of data analysis and the right tool for such analytics depends on the business need. The best tools:

• Integrate data from multiple sources.
• Can perform deep analytics such as statistical analysis and data mining across diverse systems. 
• Is scalable to cope with extreme data volumes
• Is resilient to respond fast, and allows decision-automation based on the results of analytical models.

Emerging technologies make analysing unstructured data easy. 

Cognitive engines with Natural Language Processing (NLP) capabilities enable unstructured text analysis. These engines also facilitate real-time sentiment analysis. Brands can use these tools to evaluate market sentiment and make course corrections. Speech-to-text tools unstructured audio files into structured text. 

Machine learning and predictive analytics uncover deep patterns, trends, and correlations. Many of these relationships are not explicit with first-generation tools. 

Reports, visualisations and other presentation tools enable sharing of insights. Data visualisation tools especially create clear and easy-to-understand representations of the data. Stakeholders can grasp complex information using such visualisations and make informed decisions.

Link data analytics to business outcomes

Optimal ROI from data analytics depends on linking data investment with business outcomes.

Consider the demand for personalisation. 91% of customers expect brands to personalise their online shopping experience and offer relevant offers and recommendations. The key aspects of such personalisation include advertising, the online journey and recommendations. Brands have also started to personalise the physical journey. Starbucks, for instance, has launched a data-driven Artificial Intelligence (AI) algorithm that offers customers relevant recommendations based on local weather, regional trends, and other contextual variables. Many fashion retailers deploy smart mirrors. These mirrors read tags and display information while the shopper tries on the clothing. Digital shelving replaces static price tags, with dynamic pricing reflecting personal buying patterns.

Conclusion

Getting analytics right and linking it to business objectives requires physical and logical data management solutions. Logical data management adds productivity and agility gains to existing data management practices. Denodo data management solutions make it easy to organise, store, protect and maintain data throughout its lifecycle. The real-time insights on offer enable timely actions that deliver competitive advantage.