The value of analytics and big data in digital transformation
Source – betanews.com
Big data and analytics are topics firmly embedded in our business dialogue. The amount of data we’re now generating is astonishing. Cisco predicts that annual global IP traffic will reach 3.3 ZB per year by 2021 and that the number of devices connected to IP networks will be more than three times the global population by 2021, while Gartner predicts $2.5M per minute in IoT spending and 1M new IoT devices will be sold every hour by 2021. It’s testament to the speed with which digital connectivity is changing the lives of people all over the world.
Data has also evolved dramatically in recent years, in type, volume, and velocity — with its rapid evolution attributed to the widespread digitization of business processes globally. Data has become the new business currency and its further rapid increase will be key to the transformation and growth of enterprises globally, and the advancement of employees, “the digital natives.”
The Cisco Global Cloud Index points to the cloud as the top driver as exponential data center growth with cloud center traffic quadrupling in the next five years. Data generated by IoT applications (such as connected homes, smart cities and healthcare) will be 600ZB (zettabytes) per year by 2020, 39 times higher than current data center traffic which is 15.3ZB.
Big data therefore has a far-reaching impact and meaning. But how do we understand it and its benefits, along with analytics on the journey to digital transformation? Understanding the value of data is key to the successful implementation of operational strategies that facilitate agile and effective business growth.
Big data means better business
Data is an enabler of future strategies and immediate change, thanks to the power of predictive analytics and advanced data science. Correctly harnessing data can help to achieve better, fact-based decision-making and improve the overall customer experience. By using new big data technologies, organizations can answer questions in seconds rather than days, and in days rather than months. This acceleration allows businesses to enable the type of quick reactions to key business questions and challenges that can build competitive advantage and improve performance, and provide answers for complex problems or questions that have resisted analysis.
Big data and analytics are becoming closely intertwined and need to work together to deliver the promised results of big data. Traditionally, data management and analytics have resided in different parts of the organization. Breaking down organizational boundaries and creating better integration between the IT and business departments is a critical step on the road to successful transformation.
There is also a widespread realization of the need for better business analytics. These are the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. The key is integrating big data with traditional business analytics to create a data ecosystem that allows the business to generate new insights while executing on what it already knows.
Keep learning — skills are everything
Proficiency with data mining and visualization tools ranks as one of the most important skills in determining project success.
All organizations need to consistently develop new data mining skills to fully realize the business potential. A key trend in big data is machine learning. Big data experts who can harness machine learning technology to build and train predictive analytic apps such as classification, recommendation, and personalization systems are in high demand.
Statistical and quantitative analysis, which aims to understand or predict behavior or events through the use of mathematical measurements and calculations, statistical modeling and research, is also imperative to accomplishment. Other key data mining techniques that are employed industry wide include:
- Association is one of the best-known data mining techniques. With association, a pattern is discovered based on a relationship between items in the same transaction
- Classification is a classic data mining technique based on machine learning
- Clustering is a data mining technique that makes a meaningful or useful cluster of objects which have similar characteristics using the automatic technique
- Prediction is one of a data mining techniques that discovers the relationship between independent variables and relationship between dependent and independent variables
- Sequential patterns analysis seeks to discover or identify similar patterns, regular events or trends in transaction data over a business period
- Decision tree technique, the root of the decision tree is a simple question or condition that has multiple answers
Educate your stakeholders
All stakeholders need to be educated and made aware of data’s value and understand that it’s essential to business continuity and growth. But they may feel overwhelmed (and under informed) to the power and complexity of the data if it is not properly communicated and presented. Regular meetings, ideally face to face will enforce the importance of the issue and the need for their buy-in.
Deliver digital ready networks — it makes financial sense
All today’s businesses must, via network function virtualization (decreasing the amount of proprietary hardware needed to launch and operate network services), and software defined networking (that allows updates to be made in real time or as the business demands, in just a few clicks) deliver digital ready networks to gain competitive advantage.
The increased simplicity and reduced costs associated with deploying and maintaining a more digital-ready network are core benefits and therefore should be employed as a necessity to improve and enhance business efficiency.
Automation is a high priority
Automation is a high priority in accelerating digital transformation, allowing organizations to optimize their existing processes. Automation technology is IT system and process agnostic, allowing businesses to build on their systems within the existing IT environment.
In order to create a transformative environment and improve speed and quality of delivery, organizations need to integrate automation into their existing processes to increase the ability to frequently release high-quality products — and to enable revenue and profit growth.
Automation also improves operational efficiency and allows employees to focus on more rewarding tasks. With automation, cost-effective solutions are enabled for repetitive, rules-based tasks. In addition, the prospect of human error is eliminated, delivering outcomes that are 100 percent accurate. By automating tasks, companies can significantly reduce the overall process cycle.
The road towards digital transformation is a business critical one. Organizations embarking on this journey need to consider how each aspect of their business can be optimized to fulfill new digital objectives and new growth potential. Big data and analytics play a pivotal role in digital transformation, enabling organizations to optimize their existing processes and stay ahead of the competition.