At the start of 2018, I mentioned five priorities that companies should have in their 2018 big data strategic plans These were storage, architecture, demystification, operationalization and analytics as everyday business.
These priorities still hold—because if employees at all levels in your company don't understand why you are working with big data, and haven't yet seen how this data can be put to use for the business, your big data projects are failing.
One other thing to keep in mind is that strategic planning about anything is no longer the same. You simply can't roll out one to three year plans and then measure against them as time goes by without making adjustments.
Today, strategic planning moves at the same rate of change as business and markets. Managers must continuously revisit plans to see where there is "drift" from the original plan, and what they need to do to realign it with the rate of business and market change.
SEE: 60 ways to get the most value from your big data initiatives (free PDF) (TechRepublic)
With the first quarter of 2018 coming to an end, now is an excellent time to reassess the 2018 big data plan you started with to see if adjustments are needed.
What you are likely to find is that your start of year priorities haven't changed much—but that there are certain tweaks that have to be made to stay on course.
Here are five areas to pay attention to:
1. Data storage for field-based technology
As companies move toward collecting data on drones, sensors and other field-based technology, internet bandwidth constraints will lead them to storing the collected data locally, instead of attempting to transmit all data in real time to a central location. Going into 2018, common thinking was that much of this collected data would be sent to public clouds for storage. This is still the case, but bandwidth constraints affect cloud-based storage, too. The strategy adjustment for many companies will be a return to traditional distributed storage, where data locally collected is stored on field-based servers and/or disk.
2. Partnerships with cloud vendors
Especially among SMBs, businesses are shifting their applications to cloud hosting, and are confining their onsite data center activities to the maintenance of infrastructure and networks that are needed to give the company internal IT access. With this move of applications to the cloud, disaster recovery plans for big data and other forms of data must also be revised to ensure that the vendors meet corporate governance standards, are able to execute disaster recovery and failover if needed, and have SLAs that match what internal IT provides the business. What's missing in most IT strategic plans is a more robust objective that addresses vendor management and compliance with IT service levels
3. Your company's definition of ROI
Given the rapid pace of business change, it no longer works to set return on investments (ROI) for technology at the onset of considering or buying technology, and then not revisiting ROI. This is because the business value of yesterday may not be in today's ROI. CIOs should constantly be evaluating the ROI of all tech investments, and they should be prepared to alter course if ROI begins to diminish. One case in point is IoT technology that tracks foot traffic in physical retail stores and assists managers with merchandising so they display the most sought for products in the visible areas of the store. This tech no longer delivers the same impact if the retailer sees its customers shift from physical to online venues for purchases. ROI validation is now an ongoing IT process that should be reported on minimally annually, and that should be listed in the IT strategic plan.
4. How company performance is evaluated
Like ROI, measuring the company's key performance indicators for big data is also likely to shift. For example, internal research might reveal that instead of judging customer satisfaction by how many customers promote the business to others via social media, the company might see an uptick in product defects, and instead prefer to reduce the number of remanufacture material authorizations or IoT sensor alerts on products to improve customer satisfaction. Continuous vigilance over shifting KPIs, like shifting ROIs, should be written into the IT strategic plan. Ideally, performance evaluation should be revisited at least once a year.
5. Insertion of artificial intelligence and machine learning
Everybody is reading about how artificial intelligence and machine learning can help their companies, but only a few companies are inserting these technologies effectively into their business processes. CIOs and other leaders should assume a central role by actively collaborating with end business leaders to identify business use cases where these technologies could deliver value, and then try out the technology in small pilot projects. This R&D goal should be written into the strategic plan.
- How Microsoft Azure Databricks can help companies speed big data and AI adoption (TechRepublic)
- For evidence of big data success, look no further than machine learning (TechRepublic)
- How big data can break down silos and inspire collaboration within your company (TechRepublic)
- Volume, velocity, and variety: Understanding the three V's of big data (ZDNet)
- Salesforce aims to bolster analytics for business users via natural language queries, easier visualization tools (ZDNet)
Mary E. Shacklett is president of Transworld Data, a technology research and market development firm. Prior to founding the company, Mary was Senior Vice President of Marketing and Technology at TCCU, Inc., a financial services firm; Vice President of Product Research and Software Development for Summit Information Systems, a computer software company; and Vice President of Strategic Planning and Technology at FSI International, a multinational manufacturing company in the semiconductor industry. Mary is a keynote speaker and has more than 1,000 articles, research studies, and technology publications in print.