Big Data & Logistics (Part III): Roadblocks and Considerations

After covering the what and the why of Big Data in our first post, we moved on to the how in our second post titled “Big Data & Logistics (Part II): How to Implement“.

The how – We explored the ways and processes that companies should implement within their operating framework in order to utilize Big Data analysis to value-add to their business.

In general, business leaders need to establish a sound measurement of success. It is crucial for business leaders to identify and track three factors: their stakeholders, their performance and their costs. After establishing a framework to measure these factors, business leaders need to experiment with different tools for data analysis and determine the optimal platform to integrate into their business. Lastly, business leaders have to ensure a robust digital infrastructure encapsulating the business before proceeding to conduct the relevant data analysis.

In this post, we look at the key considerations business leaders must think about when integrating Big Data analysis.

 

Data-centric concerns

What area do I want to use the data in?

Data analysis is useless to your business if you do not have a broad aim. Having a well-defined aim allows data collection and analysis to disprove existing business procedures or to verify new theories. In turn, data analysis can add value to your business and even increase your advantage over competitors by opening new insights into the market. Thus, prior to implementation of data analytics in your business, time and resources should be devoted to crafting focus areas for data analysis.

Customer Habits

One major dataset that your business should collect and analyse is of customer habits. In the logistics industry, customer habits may range from the popular delivery time slots to preferred payment method. Measuring and understanding your customers’ habits can greatly contribute to your business, where measured data signals you to tweak the output of your business depending on customers’ actions.

For instance, your marketing team plans to launch a new marketing campaign to increase awareness of your logistical services and bring in more leads. Without the results of data analysis, crafting the marketing campaign will likely be according to theories generated by preferences and bias of your marketing team. These preferences and bias may not be reflective of the wider customer preferences, which may in turn lead to an ineffective marketing campaign that targets the inappropriate groups of people. However, with collection of data on popular delivery time slots as well as delivery locations, your marketing team can change the angle of the pitch in order to make your business appear more relevant and credible.

Fleet-related Details

Another major dataset that your business should analyse is of fleet-related details. Apart from measuring external contributors to your business, understanding the internal performance of your company allows you to know if your business is ready to capture new opportunities.

For instance, you can measure the distance travelled by each vehicle at different times of the day, week and month. By understanding the peak and off-peak timings of the road, you have the knowledge sufficient to modify your operations in order to reduce cost and refocus the ground crew on other pertinent issues.

How do I protect the data collected?

Understandably, the advantage that data analysis may grant upon your business may evaporate. This could be in the form of data leaks by undetected malware on your digital platforms or an act of sabotage by your employees or stakeholders. Nonetheless, these possibilities boil down to the need for data protection, which must be implemented into your business in parallel with data analytics.

One way to protect your data is by storing the data in cloud computing systems and encrypting access to it. By storing your main datasets on the cloud, you do not have to worry about losing physical copies of the data or the circulation of multiple versions of the data around your company. Also, by encrypting access to the data, you can prevent prying eyes from outside or within your company from accessing the data at best, and making off with it at worst. With 128-bit and 256-bit encryption becoming the industry standard for digital encryption, investing into data protection tools will go a long way in preventing legal and other costs from weakening your business.

Another way to protect your data is by introducing data control procedures. First, appoint a data security officer and grant the officer certain privileges in data access. This will help to narrow the access of sensitive data by your staff. Furthermore, consider granting the data security officer duties of conducting routine checks on the data usage of staff in order to identify any malpractice. Second, implement a data protection policy that guides staff on keeping personal and customer data safe. In addition, invest resources and time in training your staff with data protection knowledge in order to mitigate future risk of malpractice or mishaps when handling sensitive data.

 

Organisational concerns

What implications are there for my company if I integrate data analysis?

Results of data analysis usually reveal counterintuitive information that may oppose your existing business model. Should you decide to integrate data analysis into your business, you must be ready to face this counterintuitive information and tweak your business model in order to remain relevant to your customers and stay ahead of the competition. The implications of a modified business model may differ in breadth and depth from different companies.

First, your company may need to redefine its existing relationships with its stakeholders. To recap from the second blog post, these stakeholders include the brand owners, the forwarders, the drivers and the consignees. Suppose your data analysis reveals new trends among one of the stakeholders – perhaps your consignees’ behaviour when ordering a delivery service. If this happens, you will need to be ready to modify the way your service engage these consignees, which could be upgrading from a simple call from your drivers to update the consignees of an incoming package to implementing a tracking system for packages. This way, your consignees will perceive an increase in the value of your services and possibly allow you to retain their patronage of your services for a longer period.

Second, your company may need to reallocate resources and time to different business units. For example, your company is enjoying level an optimal level of operations for weeks and months. However, your data analysis reveals that due to seasonal changes in the market, such as a lower consumer spending due to an economic crisis, a decrease in e-commerce deliveries is to be expected. With this prediction generated from analysis of accumulated data, you are equipped with the knowledge to determine whether resources should be devoted to sales and marketing to drive up orders for your services.

Third, your company may need to hire new talent to rapidly scale the business. Converse to the previous example of a poor economic climate, your data analysis may reveal a spike in e-commerce deliveries during the December holiday. With this knowledge, rather than allowing your company to maintain its usual level of operations, you may consider hiring short-term drivers and purchasing additional vehicles to leverage on the incoming market demand. Furthermore, should you pursue this course of action, additional staff in the support role may be needed, thereby contributing to the overall need to hire new talent to expand your business and stay ahead of the challenge.

What if the mindset of the company is against data implementation?

One of the major organisational bottlenecks to integrating data analysis into your company is resistance to change among your staff. This may be especially so if your company’s operation relies mainly on physical documentation rather than digitization. As such, there may be psychological aversion among staff who are used to the prior-mentioned system may be averse to accept digitization of their role and responsibilities. This may due to reluctance to migrate existing information to the new platform, the perceived difficulty of usage of new systems, and general discomfort in abandoning old practices.

If the organisational bottleneck is determined as psychological, then it is advisable to segment your staff according to their willingness to change and the time needed to retrain them. After doing so, you may then retrain the segments of your staff in phases so that your operations do not falter as you undertake an internal conversion of both your systems and your staff.

At the end, integration of data analysis into businesses has led to revolutionary changes in logistics and efficiency in business operations. As a business leader, the obstacle of resistance to change among your staff is a hurdle that must be crossed in order to reap the benefits of efficiency.

 

Conclusion

In conclusion, the integration of data analytics into businesses can be fraught with multiple obstacles and may incur short-term losses on your business. However, by properly weighing the costs and benefits of the different considerations of the integration of data analytics, as well as implementing preventive actions to mitigate risks, the insights that data analytics can provide your business will be unparalleled.

 

Do you have other areas of consideration that business owners should think of before implementing new technology in their business? Share them with us!